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Python for Data Analysis

Analyzing data has become a necessity for every organization and Python has been a trusted language in the analytics world, due to its rich ecosystem of libraries for data science. This course is aimed at enabling you to import data, perform data wrangling and EDA (clean, analyze, visualize) using Pandas. Later you shall build and evaluate models using Scikit-Learn, Python’s machine learning library. Furthermore, you shall learn nuances of data analysis, statistical techniques and tell a story using the data. The students taking this course will be able to gain all the necessary skillset and knowledge of the libraries in the python required for a data analyst and the students will be prepared to take the official python certification exam.

  • 9 February 2024
  • 36 hours
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  • Data Analysis with Python

    Get trained by industry Experts

    Our courses are delivered by professionals with years of experience having learned first-hand the best, in-demand techniques, concepts, and latest tools.
  • Data Analysis with Python

    Official Certification curriculum

    Our curriculum is kept up to date with the latest official Certification syllabus and making you getting ready to take the exam.
  • Data Analysis with Python

    Tax Credit

    Claim up to 25% of tuition fees and education tax credit.
  • Data Analysis with Python

    Discount on Certification Voucher

    Upto 50 percent discount voucher will be provided.
  • Data Analysis with Python

    24/7 Lab access

    Our students have access to their labs and course materials at any hour of the day to maximize their learning potential and guarantee success.


Python for Data Analysis

This module presents the basic installation setup of the python, overview of python and anaconda environment, setting up jupyter notebook and explaining the dashboard of the jupyter and importing the librariesrequired to work with data.

Basic Data Types, Operators, Variables, Declare Variables, Built-in Functions, Custom Functions, String Methods, Lists , Creating Lists, Index Positions and Slicing and Dictionaries

This module covers to create a Series Object v2, Intro to Methods, Intro to Attributes, Attributes and Methods on a Series, Parameters and Arguments, Parameters and Arguments, Import Series with the pd.read_csv Function, Import Series with the read_csv Function, Use the head and tail Methods to Return Rows from Beginning and End of Dataset, The head and tail Methods, Passing Series to Python Built-In, Functions. Use the apply Method to Invoke a Function on Every Series Values snd map Method.

This module presents Intro to DataFrames, Methods and Attributes between Series and DataFrames, Differences between Shared Methods, Select Two or More Columns from a DataFrame, Add New Column to DataFrame, Create New Column from Existing Column, A Review of the value_counts Method, Drop DataFrame Rows with Null Values with the dropna Method, Delete DataFrame Rows with Missing Values,  astype MethodSort a DataFrame with the sort_values Method, Sort DataFrame Index with the sort_index Method, Rank Series Values with the rank Method.

This Module presents module's Dataset, Memory Optimization, Filter a DataFrame Based on a Condition, Filter DataFrame with More than One Condition (AND - &), Filter DataFrame with More than One Condition (OR - |), Check for Inclusion with the isin Method, Check for Null and Present DataFrame Values with the isnull and notnull Methods, Check for Duplicate DataFrame Rows with the duplicated Method, Delete Duplicate DataFrame Rows with the drop_duplicates Method and identify and Count Unique Values with the unique and nunique Methods.

This module presents to import Datasets, Use the set_index and reset_index methods to define a new DataFrame index, Retrieve Rows by Index Label with loc Accessor, Retrieve Rows by Index Position with iloc Accessor, Passing second arguments to the loc and iloc Accessors, Set New Value for a Specific Cell or Cells In a Row, Set Multiple Values in a DataFrame, Delete Rows or Columns from a DataFrame, Create Random Sample with the sample Method, Filter. Apply a Function to every DataFrame Row with the apply Method and Create a Copy of a DataFrame with the copy Method.

This module covers Intro to the Working with Text Data Section, Common String Methods, Use the str.replace method to replace all occurrences of character with another, Filter a DataFrame's Rows with String Methods, More DataFrame String Methods - strip, lstrip, and rstrip, Invoke String Methods on DataFrame Index and Columns, Split Strings by Characters with the str.split Method, More Practice with the str.split method on a Series and Exploring the expand and n Parameters of the str.split Method

This module presents Intro to the MultiIndex Module, Create a MultiIndex on a DataFrame with the set_index Method, Create a MultiIndex on a DataFrame, Extract Index Level Values with the get_level_values Method, Change Index Level Name with the set_names Method, sort_index Method on a MultiIndex DataFrame, Extract Rows from a MultiIndex DataFrame, The transpose Method on a MultiIndex DataFrame, swaplevel Method, unstack Method, pivot Method, Use the pivot_table method to create an aggregate summary of a DataFrame and pd.melt Method

This module covers intro to the GroupBy Module, First Operations with groupby Object, Retrieve a group from a GroupBy object with the get_group Method, Methods on the Groupby Object and DataFrame Columns, Grouping by Multiple Columns and Iterating through Groups.

This module presenst the idea about the Merging, Joining, and Concatenating Section, pd.concat Method, Inner Joins, Outer Joins, left_on and right_on Parameters, Merging by Indexes with the left_index and right_index Parameters, join() Method and pd.merge() Method

This module is useful to gain knowledge about Working with Dates and Times Module, Review of Python's datetime Module, pandas Timestamp Object, pandas DateTimeIndex Object, pd.to_datetime() Method, Create Range of Dates with the pd.date_range() Method, .dt Accessor, Install pandas-datareader Library, Fixing API Errors in Next Lesson, Import Financial Data Set with pandas_datareader Library, Selecting Rows from a DataFrame with a DateTimeIndex, Timestamp Object Attributes and Methods, pd.DateOffset Object, Timeseries Offsets, Timedelta Object and Timedeltas in a Dataset

This module presents the intro to the Input and Output Section, Quick Object Conversions, Export CSV File with the to_csv Method, Install xlrd and openpyxl Libraries to Read and Write Excel Files, Import Excel File into pandas with the read_excel Method and Export Excel File with the to_excel Method

This module idea about the Visualization Section, Use the plot Method to Render a Line Chart, Modifying Plot Aesthetics with matplotlib Templates, Creating Bar Graphs to Show Counts, Creating Pie Charts to Represent Proportions.

This module presenst the introduction to the Options and Settings Module, Changing pandas Options with Attributes and Dot Syntax, Changing pandas Options with Methods and precision Option.



Professional programmers who wish to expand their developer skills with Python for AI and machine learning, Data analytics, Data visualization, Programming applications, Web development and a host of other applications.
Python is an ideal tool for non programmers as it is user friendly and easy to learn. If you are a Manager, Scientist, or a Supervisor of any level , Python may become a handy tool to learn.
Career starters : For those people who are either entering the job market or are interested in making a shift in their current job status. The Python certification program can help you transition into, or start a new career with some understanding of development.
Game developers seeking to create high end visual settings. If you choose to create intense graphics then Python may be the best option for this as it provides you with libraries and powerful rendering tools.

Eligibility and Requirements

Learners need to possess an undergraduate degree or a high school diploma. No need of any professional experience is required as this is a fundamental course.



There is no prerequiste for this course. Knowledge of any programming language and idea about the networking concepts could be an advantage while learning the course.

Official Python programming certification.


Upon completing this cerification course you will:

  • Receive an industry-recognized certificate from MCIT.
  • Be prepared for the official python programming certification.


— F.A.Q —

Definitely. Please feel free to contact our office, we will be more than happy to work with you to meet your training needs.
All of our exceptionally skilled instructors have a decent experience of training and industry experience and are AW certified in the respective field. Each of them through a rigorous selection procedure that included profile screening, technical examination, and a training demo. 
Yes, there are vouchers to take the official exam.
Upon completion of the certification course classes you will be provided with an MCIT certificate.