Complete Data Analytics & Visualization With Python

Start from scratch and learn how to import, clean, manipulate, and visualize data; and also, explore Python's most popular and robust data visualization libraries, including Matplotlib, Seaborn, Bokeh, and others, to create beautiful static and interactive visualizations of categorical, aggregated, and geospatial data.

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Data Analysis and Visualization with Python

Who this course is for:

Python developers curious about the data analysis libraries.
Python developers curious about the data visualization libraries.

Anyone interested in learning Python.

Data Analysts
Anyone working with data

What you will Learn:

Python, we will be using Python3 in this course.
Data Analysis Libraries in Python such as NumPy and Pandas.

Data Visualization.

Data Visualization Libraries in Python such as Matplotlib and Seaborn.
How to use Python to manipulate & process data.

Data analysis & data visualization using Python.
How to analyze data.

Jupyter Notebooks IDE / Anaconda Distribution.

Course Benefits & Key Features

Data Analysis and Visualisation with Python’s benefits and key features.

Modules

30+ Modules.

Lessons

80+ Lessons

Practical

40+ Hands-On Labs

Life Projects

5+ Projects

Resume

CV Preparations

Job

Jobs Reference

Recording

Session Recording

Interviews

Mock Interviews

Support

On Job Supports

Membership

Membership Access

Networks

Networking

Certification

Certificate of Completion

INSTRUCTOR-LED LIVE ONLINE CLASSES

Our learn-by-building-project method enables you to build

practical or coding experience that sticks. 95% of our

learners say they have confidence and remember more

when they learn by building real world projects which is

required to work in your real life.

Get step-by-step guidance to practice your skills without getting stuck
Validate your technical problem-solving skills in a real environment
Troubleshoot complex scenarios to practice what you learned
Develop production experience that translates into real-world

.

Python Developer Program Job Outlook

.

Ranked #1 Programming
Language

TIOBE and PYPL ranks Python as the most popular global programming language.

Python Salary
Trend

The average salary for a Python Developer is $114,489 per year in the United States.

44.8% Compound annual growth rate (CAGR)

The global python market size is expected to reach USD 100.6 million in 2030.

Why Data Analysis and Visualization with Python?

Learn In-demand Skills

Those with careers in data analysis learn relevant in-demand skills that span industries and add value to every digital-enabled organization.

Earn a Higher Salary

Experienced data analysts can earn up to $112,000 per year and transition into higher-paying jobs as Senior Data Analysts, Data Scientists, or Analytics Managers.

Positive Job Outlook

The data analytics market is predicted to hit $132.90 Billion USD by 2026. COVID-19 pandemic accelerated the adoption of data analytics solutions and services.

Shape the Future

Data analysts transform organizations by capitalizing on data to improve their business decisions and solve critical real-world problems.

Become a Leader

Being a central part of an organization’s decision-making processes, analytics experts often pick up strong leadership skills as well.

Data Analysis are Constantly Evolving

Data analysis moves quickly, and data analysts are constantly learning and advancing in their careers.

GreaterHeight Certificates holders are prepared to work at companies like these.

Some Alumni Testimonies

Investing in the course "Become a Data Analyst" with GreaterHeight Academy is great value for the money and I highly recommend. The trainer is very knowledgeable, very engaging, provided us with quality training sessions on all courses and was easily acessible for queries. We also had access to the course materials and also the timely availability of the recorded videos made it easy and aided the learning process..

QUEEN OBIWULU

Team Lead, Customer Success

The training was fantastic, the instructor is an awesome lecturer, relentless and not tired in his delivery. He obviously enjoys teaching, it comes natural to him. We got more than we expected. He extended my knowledge of Excel beyond what I knew, and the courses were brilliantly delivered. They reach out, follow up, ask questions, and in fact the support has been great. They are highly recommended and I would definitely subscribe to other training programs from them.

BISOLA OGUNRO

Fraud Analytics Risk Oversight Manager

It's one thing to look for just a Data Analysis training, and it's another to get the knowledge transferred through certified professional trainers. No matter your initial level of proficiency in any of the Data Analysis tools, GreaterHeight Academy would meet you there and take you up to a highly proficienct and confident level in a short time at a reasonable pace. I learnt a lot of Data Analysis tools and skills at GreaterHeight from patient and resourceful teachers.

TUNDE MEREDITH

Operation Director - Abbfem Technology

The Data Analysis training program was one of the best I have attended. The way GreaterHeight took off with Excel and concluded the four courses with Excel was a mind blowing - it was WOW!! I concluded that I'm on the right path with the right mentor to take me from a novice to professional. GreaterHeight is the best as far as impacting Data Analysis knowledge is concern. I would shout it at the rooftop to recommend GreaterHeight to any trainee that really wants to learn.

JOHN OSI PETER

Greaterheight

I wanted to take a moment to express my deepest gratitude for the opportunity to study data analytics at GreaterHeight Academy. I am truly impressed by the level of dedication and support that the sponsor and CEO have put into this program. GreaterHeight Academy is without a doubt the best tech institution out there, providing top-notch education and resources for its students. One of the advantages of studying at GreaterHeight Academy is the access to the best tools and technologies in the field.

AYODELE PAYNE

Sales/Data Analyst

It is an unforgettable experience that will surely stand the test of time learning to become a Data Analyst with GreaterHeights Academy. The Lecture delivery was so impactful and the Trainer is vast and well knowledgeable in using the applicable tools for the Sessions. Always ready to go extra mile with you. The supports you get during and after the lectures are top notch with materials and resources available to build your confidence on and off the job.

ADEBAYO OLADEJO

Customer Service Advisor (Special Operations)

Data Analysis and Visualization with Python Courses

Start your journey to becoming a data analyst using Python - one of the most popular programming languages in the world. No prior coding experience is required; you’ll start from scratch and learn how to import, clean, manipulate, and visualize data—all integral skills for any aspiring data professional or researcher. You’ll begin your data analyst training with interactive exercises and get hands-on with some of the most popular Python libraries, including pandas, NumPy, Seaborn, and many more. You’ll learn why Python for data analysis is so popular and work with real-world datasets to grow your data manipulation and exploratory data analysis skills. As you progress through the courses, you’ll cover topics such as data manipulation and joining data. You’ll also learn key statistics skills, like hypothesis testing. Get started today, grow your Python skills, and begin your journey to becoming a confident data analyst.

Supercharge your data science skills by learning how to create data visualization in Python. Over four courses and one assessment, you’ll explore Python's most popular and robust data visualization libraries, including Matplotlib, Seaborn, Bokeh, and others, to create beautiful static and interactive visualizations of categorical, aggregated, and geospatial data. Along the way, you’ll develop the essential skills to create informative visualizations that can showcase your data, giving you the confidence to create your own data visualizations with Python. Data visualization is fast becoming an essential skill in industries as diverse as finance, education, healthcare, retail, and more. This track will help you develop practical Python data visualization skills to apply across various data-driven roles, helping you tell stories with your data.

Introduction to Python

An Introduction to Python

Python has grown to become the market leader in programming languages and the language of choice for data analysts and data scientists. Demand for data skills is rising because companies want to gain actionable insights from their data.

Discover the Python Basics

This is a Python course for beginners, and we designed it for people with no prior Python experience. It is even suitable if you have no coding experience at all. You will cover the basics of Python, helping you understand common, everyday functions and applications, including how to use Python as a calculator, understanding variables and types, and building Python lists. The first half of this course prepares you to use Python interactively and teaches you how to store, access, and manipulate data using one of the most popular programming languages in the world.

Explore Python Functions and Packages

The second half of the course starts with a view of how you can use functions, methods, and packages to use code that other Python developers have written. As an open-source language, Python has plenty of existing packages and libraries that you can use to solve your problems.

Get Started with NumPy

NumPy is an essential Python package for data science. You’ll finish this course by learning to use some of the most popular tools in the NumPy array and start exploring data in Python.

4 Modules | 6+ Hours | 4 Skills

Course Modules

An introduction to the basic concepts of Python. Learn how to use Python interactively and by using a script. Create your first variables and acquaint yourself with Python's basic data types.

Hello Python!
Your first Python code
Any comments?
Python as a calculator
Variables and Types
Variable Assignment
Calculations with variables
Other variable types
Operations with other types

Learn to store, access, and manipulate data in lists: the first step toward efficiently working with huge amounts of data.

Python Lists
Create a list
Create lists with different types
List of lists
Subsetting Lists
Subset and conquer
Slicing and dicing
Subsetting lists of lists
Manipulating Lists
Replace list elements
Extend a list
Delete list elements
Inner workings of lists

You'll learn how to use functions, methods, and packages to efficiently leverage the code that brilliant Python developers have written. The goal is to reduce the amount of code you need to solve challenging problems!

Functions
Familiar functions
Help!
Multiple arguments
Methods
String Methods
List Methods
List Methods (2)
Packages
Import package
Selective import
Different ways of importing

NumPy is a fundamental Python package to efficiently practice data science. Learn to work with powerful tools in the NumPy array, and get started with data exploration.

NumPy
Your First NumPy Array
Baseball players' height
NumPy Side Effects
Subsetting NumPy Arrays
2D NumPy Arrays
Your First 2D NumPy Array
Baseball data in 2D form
Subsetting 2D NumPy Arrays
2D Arithmetic
NumPy: Basic Statistics
Average versus median
Explore the baseball data

Intermediate Python

Improve Your Python Skills

Learning Python is crucial for any aspiring data science practitioner. Learn to visualize real data with Matplotlib’s functions and get acquainted with data structures such as the dictionary and pandas DataFrame. This four-hour intermediate course will help you to build on your existing Python skills and explore new Python applications and functions that expand your repertoire and help you work more efficiently.

Learn to Use Python Dictionaries and pandas

Dictionaries offer an alternative to Python lists, while the pandas dataframe is the most popular way of working with tabular data. In the second module of this course, you’ll find out how you can create and manipulate datasets, and how to access them using these structures. Hands-on practice throughout the course will build your confidence in each area.

Explore Python Boolean Logic and Python Loops

In the second half of this course, you’ll look at logic, control flow, filtering and loops. These functions work to control decision-making in Python programs and help you to perform more operations with your data, including repeated statements. You’ll finish the course by applying all of your new skills by using hacker statistics to calculate your chances of winning a bet.

Once you’ve completed all of the modules, you’ll be ready to apply your new skills in your job, new career, or personal project, and be prepared to move onto more advanced Python learning!

5 Modules | 6+ Hours | 5 Skills

Course Modules

Data visualization is a key skill for aspiring data scientists. Matplotlib makes it easy to create meaningful and insightful plots. In this module, you’ll learn how to build various types of plots, and customize them to be more visually appealing and interpretable.

Basic plots with Matplotlib
Line plot (1)
Line Plot (2): Interpretation
Line plot (3)
Scatter Plot (1)
Scatter plot (2)
Histogram
Build a histogram (1)
Build a histogram (2): bins
Build a histogram (3): compare
Choose the right plot (1)
Choose the right plot (2)
Customization
Labels
Ticks
Sizes
Colors
Additional Customizations
Interpretation

Learn about the dictionary, an alternative to the Python list, and the pandas DataFrame, the de facto standard to work with tabular data in Python. You will get hands-on practice with creating and manipulating datasets, and you’ll learn how to access the information you need from these data structures.

Dictionaries, Part 1
Motivation for dictionaries
Create dictionary
Access dictionary
Dictionaries, Part 2
Dictionary Manipulation (1)
Dictionary Manipulation (2)
Dictionariception
Pandas, Part 1
Dictionary to DataFrame (1)
Dictionary to DataFrame (2)
CSV to DataFrame (1)
CSV to DataFrame (2)
Pandas, Part 2
Square Brackets (1)
Square Brackets (2)
loc and iloc (1)
loc and iloc (2)
loc and iloc (3)

Boolean logic is the foundation of decision-making in Python programs. Learn about different comparison operators, how to combine them with Boolean operators, and how to use the Boolean outcomes in control structures. You'll also learn to filter data in pandas DataFrames using logic.

Comparison Operators
Equality
Greater and less than
Compare arrays
Boolean Operators
and, or, not (1)
and, or, not (2)
Boolean operators with NumPy
if, elif, else
Warmup
if
Add else
Customize further: elif
Filtering pandas DataFrames
Driving right (1)
Driving right (2)
Cars per capita (1)
Cars per capita (2)

There are several techniques you can use to repeatedly execute Python code. While loops are like repeated if statements, the for loop iterates over all kinds of data structures. Learn all about them in this module.

while loop
while: warming up
Basic while loop
Add conditionals
for loop
Loop over a list
Indexes and values (1)
Indexes and values (2)
Loop over list of lists
Loop Data Structures Part 1
Loop over dictionary
Loop over NumPy array
Loop Data Structures Part 2
Loop over DataFrame (1)
Loop over DataFrame (2)
Add column (1)
Add column (2)

This module will allow you to apply all the concepts you've learned in this course. You will use hacker statistics to calculate your chances of winning a bet. Use random number generators, loops, and Matplotlib to gain a competitive edge!

Random Numbers
Random float
Roll the dice
Determine your next move
Random Walk
The next step
How low can you go?
Visualize the walk
Distribution
Simulate multiple walks
Visualize all walks
Implement clumsiness
Plot the distribution
Calculate the odds

Introduction to Functions in Python

It's time to push forward and develop your Python chops even further. Python has tons of fantastic functions and a module ecosystem. However, as a data professional or developer, you'll constantly need to write your own functions to solve problems that are dictated by your data. You will learn the art of function writing in this first course. You'll come out of this course being able to write your very own custom functions, complete with multiple parameters and multiple return values, along with default arguments and variable-length arguments. You'll gain insight into scoping in Python, be able to write lambda functions and handle errors in your function writing practice. You'll wrap up each module by using your new skills to write functions that analyze Twitter data.

3 Modules | 4+ Hours | 3 Skills

Course Modules

You'll learn how to write simple functions, as well as functions that accept multiple arguments and return multiple values. You'll also have the opportunity to apply these new skills to questions commonly encountered by data professionals and developers.

User-defined functions
Strings in Python
Recapping built-in functions
Write a simple function
Single-parameter functions
Functions that return single values
Multiple parameters and return values
Functions with multiple parameters
A brief introduction to tuples
Functions that return multiple values
Bringing it all together
Bringing it all together (1)
Bringing it all together (2)

You'll learn to write functions with default arguments so that the user doesn't always need to specify them, and variable-length arguments so they can pass an arbitrary number of arguments on to your functions. You'll also learn about the essential concept of scope.

Scope and user-defined functions
Pop quiz on understanding scope
The keyword global
Python's built-in scope
Nested functions
Nested Functions I
Nested Functions II
The keyword nonlocal and nested functions
Default and flexible arguments
Functions with one default argument
Functions with multiple default arguments
Functions with variable-length arguments (*args)
Functions with variable-length keyword arguments (**kwargs)
Bringing it all together
Bringing it all together (1)
Bringing it all together (2)

Learn about lambda functions, which allow you to write functions quickly and on the fly. You'll also practice handling errors in your functions, which is an essential skill. Then, apply your new skills to answer data science questions.

Lambda functions
Pop quiz on lambda functions
Writing a lambda function you already know
Map() and lambda functions
Filter() and lambda functions
Reduce() and lambda functions
Introduction to error handling
Pop quiz about errors
Error handling with try-except
Error handling by raising an error
Bringing it all together
Bringing it all together (1)
Bringing it all together (2)
Bringing it all together (3)
Bringing it all together: testing your error handling skills

Python Toolbox

In this Python Toolbox course, you'll continue to build more advanced Python skills. First, you'll learn about iterators, objects you have already encountered in the context of for loops. You'll then learn about list comprehensions, which are extremely handy tools for all data professionals and developers working in Python. You'll end the course by working through a case study in which you'll apply all the techniques you learned in both parts of this course.

3 Modules | 4+ Hours | 3 Skills

Course Modules

You'll learn all about iterators and iterables, which you have already worked with when writing for loops. You'll learn some handy functions that will allow you to effectively work with iterators. And you’ll finish the module with a use case that is pertinent to the world of data science and dealing with large amounts of data—in this case, data from Twitter that you will load in chunks using iterators.

Introduction to iterators
Iterators vs. Iterables
Iterating over iterables (1)
Iterating over iterables (2)
Iterators as function arguments
Playing with iterators
Using enumerate
Using zip
Using * and zip to 'unzip'
Using iterators to load large files into memory
Processing large amounts of Twitter data
Extracting information for large amounts of Twitter data

In this module, you'll build on your knowledge of iterators and be introduced to list comprehensions, which allow you to create complicated lists—and lists of lists—in one line of code! List comprehensions can dramatically simplify your code and make it more efficient, and will become a vital part of your Python toolbox. You'll then learn about generators, which are extremely helpful when working with large sequences of data that you may not want to store in memory, but instead generate on the fly.

List comprehensions
Write a basic list comprehension
List comprehension over iterables
Writing list comprehensions
Nested list comprehensions
Advanced comprehensions
Using conditionals in comprehensions (1)
Using conditionals in comprehensions (2)
Dict comprehensions
Introduction to generator expressions
List comprehensions vs. generators
Write your own generator expressions
Changing the output in generator expressions
Build a generator
Wrapping up comprehensions and generators.
List comprehensions for time-stamped data
Conditional list comprehensions for time-stamped data

This module will allow you to apply your newly acquired skills toward wrangling and extracting meaningful information from a real-world dataset—the World Bank's World Development Indicators. You'll have the chance to write your own functions and list comprehensions as you work with iterators and generators to solidify your Python chops.

Welcome to the case study!
Zipping dictionaries
Writing a function to help you
Using a list comprehension
Turning this all into a DataFrame
Using Python generators for streaming data
Processing data in chunks (1)
Writing a generator to load data in chunks (2)
Writing a generator to load data in chunks (3)
Using pandas' read_csv iterator for streaming data
Writing an iterator to load data in chunks (1)
Writing an iterator to load data in chunks (2)
Writing an iterator to load data in chunks (3)
Writing an iterator to load data in chunks (4)
Writing an iterator to load data in chunks (5)
Final thoughts

Data Manipulation With Pandas

Discover Data Manipulation with pandas

With this course, you’ll learn why pandas is the world's most popular Python library, used for everything from data manipulation to data analysis. You’ll explore how to manipulate DataFrames, as you extract, filter, and transform real-world datasets for analysis.

With pandas, you’ll explore all the core data science concepts. Using real-world data, including Walmart sales figures and global temperature time series, you’ll learn how to import, clean, calculate statistics, and create visualizations—using pandas to add to the power of Python.

Work with pandas Data to Explore Core Data Science Concepts

You’ll start by mastering the pandas basics, including how to inspect DataFrames and perform some fundamental manipulations. You’ll also learn about aggregating DataFrames, before moving on to slicing and indexing.

You’ll wrap up the course by learning how to visualize the contents of your DataFrames, working with a dataset that contains weekly US avocado sales.

Learn to Manipulate DataFrames

By completing this pandas course, you’ll understand how to use this Python library for data manipulation. You’ll have an understanding of DataFrames and how to use them, as well as be able to visualize your data in Python.

4 Modules | 5+ Hours | 4 Skills

Course Modules

Let’s master the pandas basics. Learn how to inspect DataFrames and perform fundamental manipulations, including sorting rows, subsetting, and adding new columns.

Introducing DataFrames
Inspecting a DataFrame
Parts of a DataFrame
Sorting and subsetting
Sorting rows
Subsetting columns
Subsetting rows
Subsetting rows by categorical variables
New columns
Adding new columns
Combo-attack!

In this module, you’ll calculate summary statistics on DataFrame columns, and master grouped summary statistics and pivot tables.

Summary statistics
Mean and median
Summarizing dates
Efficient summaries
Cumulative statistics
Counting
Dropping duplicates
Counting categorical variables
Grouped summary statistics
What percent of sales occurred at each store type?
Calculations with .groupby()
Multiple grouped summaries
Pivot tables
Pivoting on one variable
Fill in missing values and sum values with pivot tables

Indexes are supercharged row and column names. Learn how they can be combined with slicing for powerful DataFrame subsetting.

Explicit indexes
Setting and removing indexes
Subsetting with .loc[]
Setting multi-level indexes
Sorting by index values
Slicing and subsetting with .loc and .iloc
Slicing index values
Slicing in both directions
Slicing time series
Subsetting by row/column number
Working with pivot tables
Pivot temperature by city and year
Subsetting pivot tablesp
Calculating on a pivot table

Learn to visualize the contents of your DataFrames, handle missing data values, and import data from and export data to CSV files.

Visualizing your data
Which avocado size is most popular?
Changes in sales over time
Avocado supply and demand
Price of conventional vs. organic avocados
Missing values
Finding missing values
Removing missing values
Replacing missing values
Creating DataFrames
List of dictionaries
Dictionary of lists
Reading and writing CSVs
CSV to DataFrame
DataFrame to CSV
Wrap-up

Joining Data with Pandas

Being able to combine and work with multiple datasets is an essential skill for any aspiring Data Scientist. pandas is a crucial cornerstone of the Python data science ecosystem, with Stack Overflow recording 5 million views for pandas questions. Learn to handle multiple DataFrames by combining, organizing, joining, and reshaping them using pandas. You'll work with datasets from the World Bank and the City Of Chicago. You will finish the course with a solid skillset for data-joining in pandas.

4 Modules | 5+ Hours | 4 Skills

Course Modules

Learn how you can merge disparate data using inner joins. By combining information from multiple sources you’ll uncover compelling insights that may have previously been hidden. You’ll also learn how the relationship between those sources, such as one-to-one or one-to-many, can affect your result.

Inner join
What column to merge on?
Your first inner join
Inner joins and number of rows returned
One-to-many relationships
One-to-many classification
One-to-many merge
Merging multiple DataFrames
Total riders in a month
Three table merge
One-to-many merge with multiple tables

Take your knowledge of joins to the next level. In this module, you’ll work with TMDb movie data as you learn about left, right, and outer joins. You’ll also discover how to merge a table to itself and merge on a DataFrame index.

Left join
Counting missing rows with left join
Enriching a dataset
How many rows with a left join?
Other joins
Right join to find unique movies
Popular genres with right join
Using outer join to select actors
Merging a table to itself
Self join
How does pandas handle self joins?
Merging on indexes
Index merge for movie ratings
Do sequels earn more?

In this module, you’ll leverage powerful filtering techniques, including semi-joins and anti-joins. You’ll also learn how to glue DataFrames by vertically combining and using the pandas.concat function to create new datasets. Finally, because data is rarely clean, you’ll also learn how to validate your newly combined data structures.

Filtering joins
Steps of a semi join
Performing an anti join
Performing a semi join
Concatenate DataFrames together vertically
Concatenation basics
Concatenating with keys
Verifying integrity
Validating a merge
Concatenate and merge to find common songs

In this final module, you’ll step up a gear and learn to apply pandas' specialized methods for merging time-series and ordered data together with real-world financial and economic data from the city of Chicago. You’ll also learn how to query resulting tables using a SQL-style format, and unpivot data using the melt method.

Using merge_ordered()
Correlation between GDP and S&P500
Phillips curve using merge_ordered()
merge_ordered() caution, multiple columns
Using merge_asof()
Using merge_asof() to study stocks
Using merge_asof() to create dataset
merge_asof() and merge_ordered() differences
Selecting data with .query()
Explore financials with .query()
Subsetting rows with .query()
Reshaping data with .melt()
Select the right .melt() arguments
Using .melt() to reshape government data
Using .melt() for stocks vs bond performance
Course wrap-up

Introduction to Statistics in Python

Statistics is the study of how to collect, analyze, and draw conclusions from data. It’s a hugely valuable tool that you can use to bring the future into focus and infer the answer to tons of questions. For example, what is the likelihood of someone purchasing your product, how many calls will your support team receive, and how many jeans sizes should you manufacture to fit 95% of the population? In this course, you'll discover how to answer questions like these as you grow your statistical skills and learn how to calculate averages, use scatterplots to show the relationship between numeric values, and calculate correlation. You'll also tackle probability, the backbone of statistical reasoning, and learn how to use Python to conduct a well-designed study to draw your own conclusions from data.

4 Modules | 5+ Hours | 4 Skills

Course Modules

Summary statistics gives you the tools you need to boil down massive datasets to reveal the highlights. In this module, you'll explore summary statistics including mean, median, and standard deviation, and learn how to accurately interpret them. You'll also develop your critical thinking skills, allowing you to choose the best summary statistics for your data.

What is statistics?
Descriptive and inferential statistics
Data type classification
Measures of center
Mean and median
Mean vs. median
Measures of spread
Quartiles, quantiles, and quintiles
Variance and standard deviation
Finding outliers using IQR

In this module, you'll learn how to generate random samples and measure chance using probability. You'll work with real-world sales data to calculate the probability of a salesperson being successful. Finally, you’ll use the binomial distribution to model events with binary outcomes.

What are the chances?
With or without replacement?
Calculating probabilities
Sampling deals
Discrete distributions
Creating a probability distribution
Identifying distributions
Expected value vs. sample mean
Continuous distributions
Which distribution?
Data back-ups
Simulating wait times
The binomial distribution
Simulating sales deals
Calculating binomial probabilities
How many sales will be won?

It’s time to explore one of the most important probability distributions in statistics, normal distribution. You’ll create histograms to plot normal distributions and gain an understanding of the central limit theorem, before expanding your knowledge of statistical functions by adding the Poisson, exponential, and t-distributions to your repertoire.

The normal distribution
Distribution of Amir's sales
Probabilities from the normal distribution
Simulating sales under new market conditions
Which market is better?
The central limit theorem
Visualizing sampling distributions
The CLT in action
The mean of means
The Poisson distribution
Identifying lambda
Tracking lead responses
More probability distributions
Distribution dragging and dropping
Modeling time between leads
The t-distribution

In this module, you'll learn how to quantify the strength of a linear relationship between two variables, and explore how confounding variables can affect the relationship between two other variables. You'll also see how a study’s design can influence its results, change how the data should be analyzed, and potentially affect the reliability of your conclusions.

Correlation
Guess the correlation
Relationships between variables
Correlation caveats
What can't correlation measure?
Transforming variables
Does sugar improve happiness?
Confounders
Design of experiments
Study types
Longitudinal vs. cross-sectional studies
Course Wrap up!

Introduction to Data Visualization with Seaborn

Create Your Own Seaborn Plots

Seaborn is a powerful Python library that makes it easy to create informative and attractive data visualizations. This 4-hour course provides an introduction to how you can use Seaborn to create a variety of plots, including scatter plots, count plots, bar plots, and box plots, and how you can customize your visualizations.

Turn Real Datasets into Custom Seaborn Visualizations

You’ll explore this library and create your Seaborn plots based on a variety of real-world data sets, including exploring how air pollution in a city changes through the day and looking at what young people like to do in their free time. This data will give you the opportunity to find out about Seaborn’s advantages first hand, including how you can easily create subplots in a single figure and how to automatically calculate confidence intervals.

Improve Your Data Communication Skills

By the end of this course, you’ll be able to use Seaborn in various situations to explore your data and effectively communicate the results of your data analysis to others. These skills are highly sought-after for data analysts, data scientists, and any other job that may involve creating data visualizations. If you’d like to continue your learning, this course is part of several tracks, including the Data Visualization track, where you can add more libraries and techniques to your skillset.

4 Modules | 5+ Hours | 4 Skills

Course Modules

What is Seaborn, and when should you use it? In this module, you will find out! Plus, you will learn how to create scatter plots and count plots with both lists of data and pandas DataFrames. You will also be introduced to one of the big advantages of using Seaborn - the ability to easily add a third variable to your plots by using color to represent different subgroups.

Introduction to Seaborn
Making a scatter plot with lists
Making a count plot with a list
Using pandas with Seaborn
"Tidy" vs. "untidy" data
Making a count plot with a DataFrame
Adding a third variable with hue
Hue and scatter plots
Hue and count plots

In this module, you will create and customize plots that visualize the relationship between two quantitative variables. To do this, you will use scatter plots and line plots to explore how the level of air pollution in a city changes over the course of a day and how horsepower relates to fuel efficiency in cars. You will also see another big advantage of using Seaborn - the ability to easily create subplots in a single figure!

Introduction to relational plots and subplots
Creating subplots with col and row
Creating two-factor subplots
Customizing scatter plots
Changing the size of scatter plot points
Changing the style of scatter plot points
Introduction to line plots
Interpreting line plots
Visualizing standard deviation with line plots
Plotting subgroups in line plots

Categorical variables are present in nearly every dataset, but they are especially prominent in survey data. In this module, you will learn how to create and customize categorical plots such as box plots, bar plots, count plots, and point plots. Along the way, you will explore survey data from young people about their interests, students about their study habits, and adult men about their feelings about masculinity.

Count plots and bar plots
Count plots
Bar plots with percentages
Customizing bar plots
Box plots
Create and interpret a box plot
Omitting outliers
Adjusting the whiskers
Point plots
Customizing point plots
Point plots with subgroups

In this final module, you will learn how to add informative plot titles and axis labels, which are one of the most important parts of any data visualization! You will also learn how to customize the style of your visualizations in order to more quickly orient your audience to the key takeaways. Then, you will put everything you have learned together for the final exercises of the course!

Changing plot style and color
Changing style and palette
Changing the scale
Using a custom palette
Adding titles and labels: Part 1
FacetGrids vs. AxesSubplots
Adding a title to a FacetGrid object
Adding titles and labels: Part 2
Adding a title and axis labels
Rotating x-tick labels
Putting it all together
Box plot with subgroups
Bar plot with subgroups and subplots
Wrap up!

Exploratory Data Analysis in Python

So you’ve got some interesting data - where do you begin your analysis? This course will cover the process of exploring and analyzing data, from understanding what’s included in a dataset to incorporating exploration findings into a data science workflow.

Using data on unemployment figures and plane ticket prices, you’ll leverage Python to summarize and validate data, calculate, identify and replace missing values, and clean both numerical and categorical values. Throughout the course, you’ll create beautiful Seaborn visualizations to understand variables and their relationships.

For example, you’ll examine how alcohol use and student performance are related. Finally, the course will show how exploratory findings feed into data science workflows by creating new features, balancing categorical features, and generating hypotheses from findings.

By the end of this course, you’ll have the confidence to perform your own exploratory data analysis (EDA) in Python.You’ll be able to explain your findings visually to others and suggest the next steps for gathering insights from your data!

4 Modules | 5+ Hours | 4 Skills

Course Modules

What's the best way to approach a new dataset? Learn to validate and summarize categorical and numerical data and create Seaborn visualizations to communicate your findings.

Initial exploration
Functions for initial exploration
Counting categorical values
Global unemployment in 2021
Data validation
Detecting data types
Validating continents
Validating range
Data summarization
Summaries with .groupby() and .agg()
Named aggregations
Visualizing categorical summaries

Exploring and analyzing data often means dealing with missing values, incorrect data types, and outliers. In this module, you’ll learn techniques to handle these issues and streamline your EDA processes!

Addressing missing data
Dealing with missing data
Strategies for remaining missing data
Imputing missing plane prices
Converting and analyzing categorical data
Finding the number of unique values
Flight duration categories
Adding duration categories
Working with numeric data
Flight duration
Adding descriptive statistics
Handling outliers
What to do with outliers
Identifying outliers
Removing outliers

Variables in datasets don't exist in a vacuum; they have relationships with each other. In this module, you'll look at relationships across numerical, categorical, and even DateTime data, exploring the direction and strength of these relationships as well as ways to visualize them.

Patterns over time
Importing DateTime data
Updating data type to DateTime
Visualizing relationships over time
Correlation
Interpreting a heatmap
Visualizing variable relationships
Visualizing multiple variable relationships
Factor relationships and distributions
Categorical data in scatter plots
Exploring with KDE plots

Exploratory data analysis is a crucial step in the data science workflow, but it isn't the end! Now it's time to learn techniques and considerations you can use to successfully move forward with your projects after you've finished exploring!

Considerations for categorical data
Checking for class imbalance
Cross-tabulation
Generating new features
Extracting features for correlation
Calculating salary percentiles
Categorizing salaries
Generating hypotheses
Comparing salaries
Choosing a hypothesis
Recap!

Sampling in Python

Sampling in Python is the cornerstone of inference statistics and hypothesis testing. It's a powerful skill used in survey analysis and experimental design to draw conclusions without surveying an entire population. In this Sampling in Python course, you’ll discover when to use sampling and how to perform common types of sampling—from simple random sampling to more complex methods like stratified and cluster sampling. Using real-world datasets, including coffee ratings, Spotify songs, and employee attrition, you’ll learn to estimate population statistics and quantify uncertainty in your estimates by generating sampling distributions and bootstrap distributions.

4 Modules | 5+ Hours | 4 Skills

Course Modules

Learn what sampling is and why it is so powerful. You’ll also learn about the problems caused by convenience sampling and the differences between true randomness and pseudo-randomness.

Sampling and point estimates
Reasons for sampling
Simple sampling with pandas
Simple sampling and calculating with NumPy
Convenience sampling
Are findings from the sample generalizable?
Are these findings generalizable?
Pseudo-random number generation
Generating random numbers
Understanding random seeds

It’s time to get hands-on and perform the four random sampling methods in Python: simple, systematic, stratified, and cluster.

Simple random and systematic sampling
Simple random sampling
Systematic sampling
Is systematic sampling OK?
Stratified and weighted random sampling
Which sampling method?
Proportional stratified sampling
Equal counts stratified sampling
Weighted sampling
Cluster sampling
Benefits of clustering
Performing cluster sampling
Comparing sampling methods
3 kinds of sampling
Comparing point estimates

Let’s test your sampling. In this module, you’ll discover how to quantify the accuracy of sample statistics using relative errors, and measure variation in your estimates by generating sampling distributions.

Relative error of point estimates
Calculating relative errors
Relative error vs. sample size
Creating a sampling distribution
Replicating samples
Replication parameters
Approximate sampling distributions
Exact sampling distribution
Generating an approximate sampling distribution
Exact vs. approximate
Standard errors and the Central Limit Theorem
Population & sampling distribution means
Population & sampling distribution variation

You’ll get to grips with resampling to perform bootstrapping and estimate variation in an unknown population. You’ll learn the difference between sampling distributions and bootstrap distributions using resampling.

Introduction to bootstrapping
Principles of bootstrapping
With or without replacement?
Generating a bootstrap distribution
Comparing sampling and bootstrap distributions
Bootstrap statistics and population statistics
Sampling distribution vs. bootstrap distribution
Compare sampling and bootstrap means
Compare sampling and bootstrap standard deviations
Confidence intervals
Confidence interval interpretation
Calculating confidence intervals
Recap!

Hypothesis Testing in Python

Hypothesis testing lets you answer questions about your datasets in a statistically rigorous way. In this course, you'll grow your Python analytical skills as you learn how and when to use common tests like t-tests, proportion tests, and chi-square tests. Working with real-world data, including Stack Overflow user feedback and supply-chain data for medical supply shipments, you'll gain a deep understanding of how these tests work and the key assumptions that underpin them. You'll also discover how non-parametric tests can be used to go beyond the limitations of traditional hypothesis tests.

4 Modules | 5+ Hours | 4 Skills

Course Modules

How does hypothesis testing work and what problems can it solve? To find out, you’ll walk through the workflow for a one sample proportion test. In doing so, you'll encounter important concepts like z-scores, p-values, and false negative and false positive errors.

Hypothesis tests and z-scores
Uses of A/B testing
Calculating the sample mean
Calculating a z-score
p-values
Criminal trials and hypothesis tests
Left tail, right tail, two tails
Calculating p-values
Statistical significance
Decisions from p-values
Calculating a confidence interval
Type I and type II errors

In this module, you’ll learn how to test for differences in means between two groups using t-tests and extend this to more than two groups using ANOVA and pairwise t-tests.

Performing t-tests
Hypothesis testing workflow
Two sample mean test statistic
Calculating p-values from t-statistics
Why is t needed?
The t-distribution
From t to p
Paired t-tests
Is pairing needed?
Visualizing the difference
Using ttest()
ANOVA tests
Visualizing many categories
Conducting an ANOVA test
Pairwise t-tests

Now it’s time to test for differences in proportions between two groups using proportion tests. Through hands-on exercises, you’ll extend your proportion tests to more than two groups with chi-square independence tests, and return to the one sample case with chi-square goodness of fit tests.

One-sample proportion tests
t for proportions?
Test for single proportions
Two-sample proportion tests
Test of two proportions
proportions_ztest() for two samples
Chi-square test of independence
The chi-square distribution
How many tails for chi-square tests?
Performing a chi-square test
Chi-square goodness of fit tests
Visualizing goodness of fit
Performing a goodness of fit test

Finally, it’s time to learn about the assumptions made by parametric hypothesis tests, and see how non-parametric tests can be used when those assumptions aren't met.

Assumptions in hypothesis testing
Common assumptions of hypothesis tests
Testing sample size
Non-parametric tests
Which parametric test?
Wilcoxon signed-rank test
Non-parametric ANOVA and unpaired t-tests
Wilcoxon-Mann-Whitney
Kruskal-Wallis
Recap!

Introduction to Data Visualization with Matplotlib

Visualizing data in plots and figures exposes the underlying patterns in the data and provides insights. Good visualizations also help you communicate your data to others, and are useful to data analysts and other consumers of the data. In this course, you will learn how to use Matplotlib, a powerful Python data visualization library. Matplotlib provides the building blocks to create rich visualizations of many different kinds of datasets. You will learn how to create visualizations for different kinds of data and how to customize, automate, and share these visualizations.

4 Modules | 5+ Hours | 4 Skills

Course Modules

This module introduces the Matplotlib visualization library and demonstrates how to use it with data.

Introduction to data visualization with Matplotlib
Using the matplotlib.pyplot interface
Adding data to an Axes object
Customizing your plots
Customizing data appearance
Customizing axis labels and adding titles
Small multiples
Creating a grid of subplots
Creating small multiples with plt.subplots
Small multiples with shared y axis

Time series data is data that is recorded. Visualizing this type of data helps clarify trends and illuminates relationships between data.

Plotting time-series data
Read data with a time index
Plot time-series data
Using a time index to zoom in
Plotting time-series with different variables
Plotting two variables
Defining a function that plots time-series data
Using a plotting function
Annotating time-series data
Annotating a plot of time-series data
Plotting time-series: putting it all together

Visualizations can be used to compare data in a quantitative manner. This module explains several methods for quantitative visualizations.

Quantitative comparisons: bar-charts
Bar chart
Stacked bar chart
Quantitative comparisons: histograms
Creating histograms
"Step" histogram
Statistical plotting
Adding error-bars to a bar chart
Adding error-bars to a plot
Creating boxplots
Quantitative comparisons: scatter plots
Simple scatter plot
Encoding time by color

This module shows you how to share your visualizations with others: how to save your figures as files, how to adjust their look and feel, and how to automate their creation based on input data.

Preparing your figures to share with others
Selecting a style for printing
Switching between styles
Saving your visualizations
Saving a file several times
Save a figure with different sizes
Automating figures from data
Unique values of a column
Automate your visualization
Where to go next

Improving Your Data Visualizations in Python

Great data visualization is the cornerstone of impactful data science. Visualization helps you to both find insight in your data and share those insights with your audience. Everyone learns how to make a basic scatter plot or bar chart on their journey to becoming a data scientist, but the true potential of data visualization is realized when you take a step back and think about what, why, and how you are visualizing your data. In this course you will learn how to construct compelling and attractive visualizations that help you communicate the results of your analyses efficiently and effectively. We will cover comparing data, the ins and outs of color, showing uncertainty, and how to build the right visualization for your given audience through the investigation of a datasets on air pollution around the US and farmer's markets. We will finish the course by examining open-access farmers market data to build a polished and impactful visual report.

4 Modules | 5+ Hours | 4 Skills

Course Modules

How do you show all of your data while making sure that viewers don't miss an important point or points? Here we discuss how to guide your viewer through the data with color-based highlights and text. We also introduce a dataset on common pollutant values across the United States.

Highlighting data
Hardcoding a highlight
Programmatically creating a highlight
Comparing groups
Comparing with two KDEs
Improving your KDEs
Beeswarms
Annotations
A basic text annotation
Arrow annotations
Combining annotations and color

Color is a powerful tool for encoded values in data visualization. However, with this power comes danger. In this module, we talk about how to choose an appropriate color palette for your visualization based upon the type of data it is showing.

Color in visualizations
Getting rid of unnecessary color
Fixing Seaborn's bar charts
Continuous color palettes
Making a custom continuous palette
Customizing a diverging palette heatmap
Adjusting your palette according to context
Categorical palettes
Using a custom categorical palette
Dealing with too many categories
Coloring ordinal categories
Choosing the right variable to encode with color

Uncertainty occurs everywhere in data science, but it's frequently left out of visualizations where it should be included. Here, we review what a confidence interval is and how to visualize them for both single estimates and continuous functions. Additionally, we discuss the bootstrap resampling technique for assessing uncertainty and how to visualize it properly.

Point estimate intervals
Basic confidence intervals
Annotating confidence intervals
Confidence bands
Making a confidence band
Separating a lot of bands
Cleaning up bands for overlaps
Beyond 95%
90, 95, and 99% intervals
90 and 95% bands
Using band thickness instead of coloring
Visualizing the bootstrap
The bootstrap histogram
Bootstrapped regressions
Lots of bootstraps with beeswarms

Often visualization is taught in isolation, with best practices only discussed in a general way. In reality, you will need to bend the rules for different scenarios. From messy exploratory visualizations to polishing the font sizes of your final product; in this module, we dive into how to optimize your visualizations at each step of a data science workflow.

First explorations
Looking at the farmers market data
Scatter matrix of numeric columns
Digging in with basic transforms
Exploring the patterns
Is latitude related to months open?
What state is the most market-friendly?
Popularity of goods sold by state
Making your visualizations efficient
Stacking to find trends
Using a plot as a legend
Tweaking your plots
Cleaning up the background
Remixing a plot
Enhancing legibility
Recap!

Visualizing Geospatial Data in Python

One of the most important tasks of a data scientist is to understand the relationships between their data's physical location and their geographical context. In this course you'll be learning to make attractive visualizations of geospatial data with the GeoPandas package. You will learn to spatially join datasets, linking data to context. Finally you will learn to overlay geospatial data to maps to add even more spatial cues to your work. You will use several datasets from the City of Nashville's open data portal to find out where the chickens are in Nashville, which neighborhood has the most public art, and more!

4 Modules | 5+ Hours | 4 Skills

Course Modules

In this module, you will learn how to create a two-layer map by first plotting regions from a shapefile and then plotting location points as a scatterplot.

Introduction
Plotting a scatterplot from longitude and latitude
Styling a scatterplot
Extracting longitude and latitude
Plotting chicken locations
Geometries and shapefiles
Creating a GeoDataFrame & examining the geometry
Plotting shapefile polygons
Scatterplots over polygons
Geometry
Plotting points over polygons - part 1
Plotting points over polygons - part 2

You'll work with GeoJSON to create polygonal plots, learn about projections and coordinate reference systems, and get practice spatially joining data in this module.

GeoJSON and plotting with geopandas
Working with GeoJSON
Colormaps
Map Nashville neighborhoods
Projections and coordinate reference systems
Changing coordinate reference systems
Construct a GeoDataFrame from a DataFrame
Spatial joins
Spatial join practice
Finding the neighborhood with the most public art
Aggregating points within polygons
Plotting the Urban Residents neighborhood and art

First you will learn to get information about the geometries in your data with three different GeoSeries attributes and methods. Then you will learn to create a street map layer using folium.

GeoSeries attributes and methods I
Find the area of the Urban Residents neighborhood
GeoSeries attributes and methods II
The center of the Urban Residents neighborhood
Prepare to calculate distances
Art distances from neighborhood center
Street maps with folium
Create a folium location from the urban centroid
Create a folium map of downtown Nashville
Folium street map of the downtown neighborhood
Creating markers and popups in folium
Adding markers for the public art
Troubleshooting data issues
A map of downtown art

In this module, you will learn about a special map called a choropleth. Then you will learn and practice building choropleths using two different packages: geopandas and folium.

What is a choropleth?
Finding counts from a spatial join
Council district areas and permit counts
Calculating a normalized metric
Choropleths with geopandas
Geopandas choropleths
Area in km squared, geometry in decimal degrees
Spatially joining and getting counts
Building a polished Geopandas choropleth
Choropleths with folium
Folium choropleth
Folium choropleth with markers and popups
Closing thoughts

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Complete Data Analysis & Visualization with Python Course

Python, created by Guido van Rossum in 1991, is a high-level, readable programming language known for its simplicity. It's versatile, with applications in web development, data analysis, AI, and more. Python's extensive standard library and rich ecosystem enhance its capabilities. It's cross-platform compatible and supported by a large community. Python's popularity has grown, making it widely used in diverse industries.

A Python developer is a software developer or programmer who specializes in using the Python programming language for creating applications, software, or solutions. They have expertise in writing Python code, understanding the language's syntax, libraries, and frameworks. Python developers are skilled in utilizing Python's features to develop web applications, data analysis tools, machine learning models, automation scripts, and other software solutions.
They work in various industries, collaborating with teams or independently to design, implement, test, and maintain Python-based projects. Python developers often possess knowledge of related technologies and tools to enhance their development process.

Python Developer Masters Program is a structured learning path recommended by leading industry experts and ensures that you transform into a proficient Python Developer. Being a full fledged Python Developer requires you to master multiple technologies and this program aims at providing you an in-depth knowledge of the entire Python programming practices. Individual courses at GreaterHeight Academy focus on specialization in one or two specific skills; however, if you intend to become a master in Python programming then this is your go to path to follow.

Yes. But you can also raise a ticket with the dedicated support team at any time. If your query does not get resolved through email, we can also arrange one-on-one sessions with our support team. However, our support is provided for a period of Twelve Weeks from the start date of your course.

There are several reasons why becoming a Python developer can be a rewarding career choice. Here are a few:

Versatility and Popularity: Python is a versatile programming language that can be used for various purposes, such as web development, data analysis, machine learning, artificial intelligence, scientific computing, and more. It has gained immense popularity in recent years due to its simplicity, readability, and extensive library ecosystem. Python is widely used in both small-scale and large-scale projects, making it a valuable skill in the job market.
Ease of Learning: Python has a clean and intuitive syntax that emphasizes readability, which makes it relatively easy to learn compared to other programming languages. Its simplicity allows beginners to grasp the fundamentals quickly and start building useful applications in a relatively short amount of time. This accessibility makes Python an attractive choice for both novice and experienced programmers.
Rich Ecosystem and Libraries: Python offers a vast collection of libraries and frameworks that can accelerate development and simplify complex tasks. For example, Django and Flask are popular web development frameworks that provide robust tools for building scalable and secure web applications. NumPy, Pandas, and Matplotlib are widely used libraries for data analysis and visualization. TensorFlow and PyTorch are prominent libraries for machine learning and deep learning. These libraries, among many others, contribute to Python's efficiency and effectiveness as a development language.
Job Opportunities: The demand for Python developers has been steadily growing in recent years. Many industries, including technology, finance, healthcare, and academia, rely on Python for various applications. By becoming a Python developer, you open up a wide range of career opportunities, whether you choose to work for a large corporation, a startup, or even as a freelancer. Additionally, Python's versatility allows you to explore different domains and switch roles if desired.
Community and Support: Python has a vibrant and supportive community of developers worldwide. This community actively contributes to the language's development, creates open-source libraries, and provides assistance through forums, online communities, and resources.

There are no prerequisites for enrollment to this Masters Program. Whether you are an experienced professional working in the IT industry or an aspirant planning to enter the world of Python programming, this masters program is designed and developed to accommodate various professional backgrounds.

Python Developer Masters Program has been curated after thorough research and recommendations from industry experts. It will help you differentiate yourself with multi-platform fluency and have real-world experience with the most important tools and platforms. GreaterHeight Academy will be by your side throughout the learning journey - We’re Ridiculously Committed.

The recommended duration to complete this Python Developer Masters Program is about 20 weeks, however, it is up to the individual to complete this program at their own pace.

The roles and responsibilities of a Python developer may vary depending on the specific job requirements and industry. However, here are some common tasks and responsibilities associated with the role:

Developing Applications: Python developers are responsible for designing, coding, testing, and debugging applications using Python programming language. This includes writing clean, efficient, and maintainable code to create robust software solutions.
Web Development: Python is widely used for web development. As a Python developer, you may be involved in building web applications, using frameworks like Django or Flask. This includes developing backend logic, integrating databases, handling data processing, and ensuring the smooth functioning of the web application.
Data Analysis and Visualization: Python offers powerful libraries like NumPy, Pandas, and Matplotlib, which are extensively used for data analysis and visualization. Python developers may be responsible for manipulating and analyzing large datasets, extracting insights, and presenting them visually.
Machine Learning and AI: Python is a popular choice for machine learning and artificial intelligence projects. Python developers may work on implementing machine learning algorithms, training models, and integrating them into applications. This involves using libraries like TensorFlow, PyTorch, or scikit-learn.
Collaborating and Teamwork: Python developers often work as part of a development team. They collaborate with other team members, including designers, frontend developers, project managers, and stakeholders. Effective communication and teamwork skills are crucial to ensure smooth project execution.
Documentation: Python developers are expected to document their code, providing clear explanations and instructions for others who may work on or maintain the codebase in the future. Documentation helps in understanding the code and facilitating collaboration.
Continuous Learning: Technology is constantly evolving, and as a Python developer, you need to stay updated with the latest advancements, libraries, frameworks, and best practices. Continuous learning and self-improvement are essential to excel in this role.

The Python Developer training course is for those who want to fast-track their Python programming career. This Python Developer Masters Program will benefit people working in the following roles:

Freshers
Engineers
IT professionals
Data Scientist
Machine Learning Engineer
AI Engineer
Business analysts
Data analysts

Top companies such as Microsoft, Google, Meta, Citibank, Well Fargo, and many more are actively hiring certified Python professionals at various positions.

On completing this Python Developer Masters Program, you’ll be eligible for the roles like: Python Developer, Web Developer, Data Analyst, Data Scientist, Software Engineer and many more.

There is undoubtedly great demand for data analytics as 96% of organizations seek to hire Data Analysts. The most significant data analyst companies that employ graduates who wish to have a data analyst career are Manthan, SAP, Oracle, Accenture Analytics, Alteryx, Qlik, Mu Sigma Analytics, Fractal Analytics, and Tiger Analytics. Professional Data Analyst training will make you become a magician of any organization, and you will spin insights by playing with big data.

A successful data analyst possesses a combination of technical skills and leadership skills.

Technical skills include knowledge of database languages such as SQL, R, or Python; spreadsheet tools such as Microsoft Excel or Google Sheets for statistical analysis; and data visualization software such as Tableau or Qlik. Mathematical and statistical skills are also valuable to help gather, measure, organize, and analyze data while using these common tools.
Leadership skills prepare a data analyst to complete decision-making and problem-solving tasks. These abilities allow analysts to think strategically about the information that will help stakeholders make data-driven business decisions and to communicate the value of this information effectively. For example, project managers rely on data analysts to track the most important metrics for their projects, to diagnose problems that may be occurring, and to predict how different courses of action could address a problem.

Career openings are available practically from all industries, from telecommunications to retail, banking, healthcare, and even fitness. Without extensive training and effort, it isn't easy to get data analyst career benefits. So, earning our Data Analyst certification will allow you to keep up-to-date on recent trends in the industry.

Yes, we do. We will discuss all possible technical interview questions and answers during the training program so that you can prepare yourself for interview.

No. Any abuse of copyright is taken seriously. Thanks for your understanding on this one.

Yes, we would be providing you with the certificate of completion of the program once you have successfully submitted all the assessment and it has been verified by our subject matter experts.

GreaterHeight is offering you the most updated, relevant, and high-value real-world projects as part of the training program. This way, you can implement the learning that you have acquired in real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry ready.
You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.

All our mentors are highly qualified and experience professionals. All have at least 15-20 yrs. of development experience in various technologies and are trained by GreaterHeight Academy to deliver interactive training to the participants.

Yes, we do. As the technology upgrades, we do update our content and provide your training on latest version of that technology.

All online training classes are recorded. You will get the recorded sessions so that you can watch the online classes when you want. Also, you can join other class to do your missing classes.

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Complete Data Analytics & Visualization With Python

Data Analysis and Visualization with Python

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Data Analysis and Visualisation with Python’s benefits and key features.

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Data Analysis and Visualization with Python Courses

Module 1: Python Basics

Module 2: Python Lists

Module 3: Functions and Packages

Module 4: NumPy

Module 1: Matplotlib

Module 2: Dictionaries & Pandas

Module 3: Logic, Control Flow and Filtering

Module 4: Loops

Module 5: Case Study: Hacker Statistics

Module 1: Writing Your Own Functions

Module 2: Default Arguments, Variable-Length Arguments and Scope

Module 3: Lambda Functions and Error-Handling

Module 1: Using iterators in PythonLand

Module 2: List Comprehensions and Generators

Module 3: Bringing it all Together!

Module 1: Transforming DataFrames

Module 2: Aggregating DataFrames

Module 3: Slicing and Indexing DataFrames

Module 4: Creating and Visualizing DataFrames

Module 1: Data Merging Basics

Module 2: Merging Tables With Different Join Types

Module 3: Advanced Merging and Concatenating

Module 4: Merging Ordered and Time-Series Data

Module 1: Summary Statistics

Module 2: Random Numbers and Probability

Module 3: More Distributions and the Central Limit Theorem

Module 4: Correlation and Experimental Design

Module 1: Introduction to Seaborn

Module 2: Visualizing Two Quantitative Variables

Module 3: Visualizing a Categorical and a Quantitative Variable

Module 4: Customizing Seaborn Plots

Module 1: Getting to Know a Dataset

Module 2: Data Cleaning and Imputation

Module 3: Relationships in Data

Module 4: Turning Exploratory Analysis into Action

Module 1: Introduction to Sampling

Module 2: Sampling Methods

Module 3: Sampling Distributions

Module 4: Bootstrap Distributions

Module 1: Hypothesis Testing Fundamentals

Module 2: Two-Sample and ANOVA Tests

Module 3: Proportion Tests

Module 4: Non-Parametric Tests

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