Understanding the Business Goal and analyzing the features/variables/columns which we need for the data analysis.

1. Core Python

Basics (Installation and Usage)

Data & Primitive Data Types (Integer, Float, String, Bool,

Complex)

Variables & Operators with data

Input & Output

Type Casting

Conditions ( While, If – elif – else )

2. Data Structures

• Strings as Data Structure, List, Tuple, Dictionary, Set
• Properties of Data Structure
• Indexing & Slicing
• Data Structure Methods

3. Loops

• For , while
• List Comprehensions
• Break, Continue, Pass

4. Functions

• User defined functions, Built-in Functions
• Generators & Decorators
• LambdaMap , Filter, Reduce

5. Advanced Python

• OOP
• Object & Class (Inheritance, Polymorphism, Encapsulation,
• Abstraction) 
• Class Methods & Static Methods
• Modules & Libraries
• Modules
• Parsing Arguments
• Libraries for Data Science
• Exception Handling
• File Handling
• Multi-Threading & Multi-Processing

1. Primary

• Manual Data Collection

2. Secondary

• Open Sources: Kaggle, UCI machine learning repository,
• Google Data Search
• Databases: SQL (Import and export)
• Web scraping: Extracting data from potential website

1. Validating Collected Column/Features data

• Removing Extra Spaces or characters in column data if necessary

1. EDA (Uni-Variate, Bi-Variate, & Multi-Variate)

• Statistical Analysis
  • Variables
  • Variable Data Types Study
  • Descriptive Stats
  • Measure of Central Tendency(Mean, Median, Mode)
  • Measure of Dispersion/Spread (Range, Standard Deviation,
• Variance, Quartiles, IQR)
  • Measure of Symmetry (Skewness and Kurtosis) 
  • Correlation, Co-linearity & Covariance
• Visual Analysis
  • Bar plots, Boxplots, Pie charts, Histogram, Distplots
  • Scatterplots, Pair plots, Heat maps
• Inferential Stats
  • Population and sample
  • Probability, Distributions & Z scores 
  • Confidence Interval  Central Limit theorem
  • Hypothesis Testing (Z tests, Anova, Chi-Square)

F. Data Preparation for Predictive Modeling

1. Missing Values & Outliers Handling
2. Input (X) & Output (y)
3. ls selection
4. Feature Engineering

• Feature Selection
• Feature Generation
• Feature modification
  • Data-Preprocessing
  • Scaling (Scaling Numeric Data)
  • Encoding (Converting Categorical Data to Numerical)
• Data Leakage

1. Basics (Data Bases, Tables, Data Base Management System(DBMS), SQL)

2. Working with Database Management System

• RDBMS (MySQL) ( SQL, Queries (DDL, DML, Joins, and
• Aggregations using Group by, Filters…) )
• NRDBMS (MongoDB) – Non-relational Databases (No-SQL,Queries (CRUD))

1. Modeling Basics

• Predictive Modeling & AI Introduction

2. Machine Learning (ML)

• Train-Test Split
• Supervised Learning & Its Algorithms
  • Regression
    • Linear Regression (Simple & Multiple, Polynomial Regression, Lasso(L1) & Ridge(L2))
    • Non-Linear Regression (Random forest,Decision Tree Regressor, Svm Regressor, etc. )

3. Classification

• Class Imbalance
• Logistic Regression
• SVM
• KNNNaïve Bayes
• Decision Tree
• Random Forest
• Boosting and Baggin

4. Model Evaluation Techniques

• Regression and Classification Metrics 
• Cross-validation
• Bias-Variance Trade off
• Hyper-Parameter Tuning
• Un- Supervised Learning & It’s algorithms
  • Dimensionality Reduction (PCA)
  • Clustering( K-means, H-Clustering, DBSCAN Clustering)

5. Deep Learning (DL)

• Introduction to Neural Networks
  • Neurons in Human Brain ( What are neural network, structure of Neural Network)
  • How Neural networks work (Feed Forward Neural Network)
  • Forward Propagation (Weights)
  • Backward Propagation (Optimization (Gradient Descent))

6. Types of Neural Networks

• Artificial Neural Network (ANN)
• Convolution Neural Network (CNN)
• Recurrent Neural Network (RNN)

7. ANN for Regression & Classification

8. Working with Images Data

9. Introduction to CNN

• CNN Architectures

10. Image Classification using CNN

11. Introduction to Object Detection

• Object Detection Vs Object Classification 
• OpenCv Object Detection

12. Object detection models

• Introduction to R-CNN, Faster R-CNN, YOLO etc….
• Introduction to transfer learning Custom Object
• Detection using YOLO recent version

13. Working with Text data (Natural Language Understanding (NLU))

• Collecting Text data according to Analysis
• Text validation & Cleaning (Tokenization, Stop words, Stemming/Lemmatization)
• Text Pre-Processing (Converting Text to Numeric vectors (N-grams, BOW , TF-IDF, Word2vec Word Embedding’s)

14. Language Modeling (Natural Language Generation (NLG))

Sentiment Analysis/Text Classification Predictive

Modeling using ANN/ML o Introduction to Sequence Models

• RNN Sequence data (Text/Time series)
• Using RNN for Sequence data- LSTM
• Model Types Based on RNN/LSTM
• Next word prediction/Translations/Q&A Predictive Modeling using RNN/LSTM

1. Introduction

2. Tableau

• Introduction
• Installation
• Data Sources
• Visualizations
• Filters
• Dashboard
• Story

3. PowerBi

• Introduction
• Installation
• Data Sources
• Power Query
• Visualizations, Interactions Measures
• DAX (Data Analysis Expressions)
• Filters
• Reports