Complete Data Science

The Ultimate Guide for Interview preparation

Machine Learning, AI, Data Analytics, Python programming

Module 1 — Data Science Overview

• Introduction to Data Science
• Different Sectors Using Data Science
• Purpose and Components of Python

Module 2 — Data Analytics Overview

• Data Analytics Process
• Exploratory Data Analysis(EDA)
• EDA-Quantitative Technique
• EDA — Graphical Technique
• Data Analytics Conclusion or Predictions
• Data Analytics Communication
• Data Types for Plotting
• Data Types and Plotting

Module 3 — Python Environment Setup and Essentials

• Anaconda
• Installation of Anaconda Python Distribution (contd.)
• Data Types with Python
• Basic Operators and Functions

Module 4 — Python Language Fundamentals

• If statement
• If else statement
• If elif statement
• If elif else statement
• Nested if statement
• While loop
• For loop
• Nested loops
• Pass, break and continue keywords
• Datatypes — Int, float, bool, string, date
• String Handling
• List
• Tuple
• Dictionary
• Functions
• Exception Handling

Module 5 — Mathematical Computing with Python (NumPy)

• Introduction to Numpy
• Activity-Sequence
• Creating and Printing an ndarray
• Class and Attributes of ndarray
• Basic Operations
• Activity-Slice It
• Copy and Views
• Mathematical Functions of Numpy

Module 6— Data Manipulation with Pandas

• Introduction to Pandas
• Understanding DataFrame
• Missing Values
• Data Operations
• File Read and Write Suppor
• Pandas SQL Operation
• Analyze the E-commerce Dataset using Pandas
• Analyze the Stock market Dataset using Pandas

Module 7— Data Visualization in Python

• Introduction to Data Visualization
• Line Properties
• (x,y) Plot and Subplots
• Types of Plots
• Draw a pair plot using seaborn library
• Analysing Covid Dataset

Module 8— Machine Learning with Scikit–Learn

• Machine Learning Approach
• Supervised Learning Models
• Unsupervised Learning Models
• Pipeline
• Model Persistence and Evaluation
• Building a model to predict Diabetes