DURATION: 5 Day (40 hours)
Target Audience:
Data Analyst, Business Analysts, Data Scientist.
Course Outcomes:
- Master Python fundamentals for data manipulation and analysis.
- Explore data types, control flows, and operators in Python.
- Gain proficiency in data pre-processing and cleaning techniques.
- Perform exploratory data analysis using Pandas and NumPy.
- Develop skills in data visualization with Matplotlib.
Module: Introduction to Python and Basics:
- Definition & Applications.
- Features, Versions & Working.
- Anaconda & Different IDEs for Python.
- Introduction to IDE’s – Jupiter Notebook, Spider & Google Colab.
Module: Data Types and Control Flows:
- Literals, reserved words and input functions.
- Data Types: into, float, bool, star.
- Decision Control Flows: If / Nested If / If-else / If-elif-else.
- Control Flow Loops: While loop, For loop, While-else, For-else.
- Operators: Arithmetic, Relational or Comparison, Logical.
Module: Lists, Tuples, Sets, and Dictionaries:
- Bitwise Operators, Assignment Operators, Ternary Operator.
- List: Ways of Accessing Values, Traversing Elements, List Operations, List Methods, Membership Operator, List Comprehension.
- Tuples: Creating Tuples, Ways of Accessing Values, Tuple Vs Immutability, Tuple Comprehension.
- Sets: Creating Sets, Ways of Accessing Values, Manipulating and Accessing Sets, Set Operations.
- Dictionary: Why Dictionary, creating a Dictionary, Accessing Values, Updating Dictionaries, Functions of Dictionary.
Module: File Handling and Strings:
- File Handling: Types of Files, Opening and Closing Files, Writing, Appending, and Reading Files.
- Strings: String Literals, Single (”) & Double Quotes (“”), Triple Quotes (”’), Raw Strings (“r’…’ “) and Operations on strings.
- Dictionary: Accessing Values, Updating Dictionaries, Functions of Dictionary.
Module: Iterators & Generators:
- Iterator vs Inerrable, Containers, Generators in Python.
Module: Regular Expressions:
- Uses of Regular Expressions – Text Analytics, import re, Character Classes, Backslash, Alteration, Quantifiers.
Module: OOPS Concept:
- Class, Classes and Object, Creating Object, Accessing Objects, Need and Use of Self, Class Method, __in it__() constructor.
Module: Introduction to NumPy, Pandas & Matplotlib:
- Introduction to NumPy, Install NumPy.
- Array Creation, Array Reshaping, Indexing, Operations.
- Introduction to Pandas, Slicing Data, Slicing Data Frame.
- Data Visualization with Matplotlib.
Module: Introduction to Data Pre-processing:
- Filtering Data Frame, Transforming Data Frame, Advanced Indexing.
Data Cleaning & Data Pre-processing.
Module: Exploratory Data Analysis (EDA):
- Data Cleaning Techniques, Handling Missing Data, Handling Categorical Data.
- Introduction to EDA, 2D Scatter-plot, 3D Scatter-plot, Pair plots.
- Univariate, Bivariate, and Multivariate Analysis, Box-plot.
- Variance and Standard Deviation, Median, IQR (Interquartile Range).
Advanced Pandas and Data Visualization:
- Advanced Pandas, Advanced Indexing, Data Preparation.
- Handling Missing Data, handling Categorical Data, Data Cleaning.
Data Visualization:
- Introduction to Data Visualization, Plotting with Matplotlib.
- Scatter Plots, Line Plots, Bar Plots, Pie Charts, Heat maps.
Project Work:
- Problem Statement, Data Collection, Data pre-processing (Exploratory Data Analysis), Feature Engineering (optional), Data visualizations (Pandas, NumPy & Matplotlib), Project Final Outcome & findings.
