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Data Science with Python
Duration : 25 hours


  1. A computer (Windows/Mac/Linux).
  2. No prior knowledge of python is required.
  3. No prior knowledge of coding is required.
Data Science and Data Analysis: What is Data Science?
Why Data Science?, Data Science Components, Data Science Process, Data Science Jobs Roles, Tools for DataScience, Difference Between Data Science and Data Analysis, Applications of Data Science
Introduction to Programming with Python

  • Installation of Anaconda
  • How to use Jupyter Notebooks
  • First steps with Python and Jupyter notebooks
Python Basics Practice
Your First Python Program, How Python Code Gets Executed, How Long It Takes To Learn Python, Variables, Receiving Input, Python Cheat Sheet, Type Conversion, Strings, Formatted Strings, String Methods, Arithmetic Operations, Operator Precedence, Maths Functions, If Statements, Logical Operators, Comparison Operators, Weight Converter Program, While Loops, Building a Guessing Game, For Loops, Nested Loops, Lists, 2D Lists, List Methods, Tuples, Unpacking, Dictionaries, Functions, Parameter, Keyword Arguments, Return Statement, Creating a Reusable Function, Exceptions, Comments, Classes, Constructors, Inheritance, Modules, Packages
Numerical Computing with Numpy
Numpy arrays, Multi-dimensional arrays, Array operations, slicing and broadcasting, Working with CSV data files,
Analysing Tabular Data with Pandas
Create your very first Pandas DataFrame (from csv), Pandas Display Options and the methods, Data Inspection, Built-in Functions, Attributes and Methods with Pandas, Indexing, slicing.Pandas Series: Analysing, sorting and filtering. Dataframes:Loading the data into Pandas (CSVs, Excel, TXTs, etc.)Reading Data (Getting Rows, Columns, Cells, Headers, etc.), Sorting Values (Alphabetically, Numerically), Making Changes to the DataFrame, Adding a column,Deleting a column, Summing Multiple Columns to Create new Columns, Rearranging columns, Saving our Data (CSV, Excel, TXT, etc.), Filtering Data (based on multiple conditions), Conditional Changes, Aggregate Statistics using Groupby (Sum, Mean, Counting)
Data Visualisation with Matplotlib, Seaborn, Plotly
Build an Exploratory Data Analysis Project from Scratch with Python, Numpy, and Pandas, usage of streamlit for web apps.
Optional Project: Web scraping and EDA of real life projects.

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