Curriculum
21 Sections
74 Lessons
24 Weeks
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HYPOTHESIS TESTING
5
0.0
CONCEPTS OF HYPOTHESIS TESTING
0.1
I: NULL AND ALTERNATE HYPOTHESIS, MAKING A DECISION, AND CRITICAL VALUE METHOD
0.2
CONCEPTS OF HYPOTHESIS TESTING – II: P VALUE METHOD AND TYPES OF ERRORS
0.3
INDUSTRY DEMONSTRATION OF HYPOTHESIS TESTING
0.4
TWO-SAMPLE MEAN AND PROPORTION TEST, A/B TESTING
MODEL SELCTION CASE STUDY
3
0.0
PROBLEM STATEMENT
0.1
FINAL SUBMISSION
0.2
SOLUTION
TIME SERIES FORECASTING
4
0.0
INTRODUCTION TO TIME SERIES AND ITS COMPONENTS
0.1
SMOOTHING TECHNIQUES
0.2
INTRODUCTION TO AR MODELS
0.3
BUILDING AR MODELS
MODEL SELECTION & GENERAL ML TECHNIQUES
3
0.0
PRINCIPLES OF MODEL SELECTION
0.1
MODEL EVALUATION
0.2
MODEL SELECTION: BEST PRACTICES
ADVANCED ML CASE STUDY
4
0.0
PROBLEM STATEMENT
0.1
EVALUATION RUBRIC
0.2
FINAL SUBMISSION
0.3
SOLUTION
ADVANCED REGRESSION
2
0.0
GENERALISED LINEAR REGRESSION
0.1
REGULARISED REGRESSION
PRINCIPAL COMPONENT ANALYSIS
2
0.0
PRINCIPAL COMPONENT ANALYSIS AND SINGULAR VALUE DECOMPOSITION
0.1
PRINCIPAL COMPONENT ANALYSIS IN PYTHON
BOOSTING
2
0.0
INTRODUCTION TO BOOSTING AND ADABOOST
0.1
GRADIENT BOOSTING
BAGGING & RANDOM FOREST
5
0.0
POPULAR ENSEMBLES
0.1
INTRODUCTION TO RANDOM FORESTS
0.2
HIERARCHICAL CLUSTERING
0.3
FEATURE IMPORTANCE IN RANDOM FORESTS
0.4
RANDOM FORESTS IN PYTHON
CASE STUDY: LEAD SCORING
4
0.0
PROBLEM STATEMENT
0.1
EVALUATION RUBRIC
0.2
FINAL SUBMISSION
0.3
SOLUTION
BUSINESS PROBLEM SOLVING
2
0.0
INTRODUCTION TO BUSINESS PROBLEM SOLVING
0.1
BUSINESS PROBLEM SOLVING: CASE STUDY DEMONSTRATIONS
BASICS OF NLP AND TEXT MINING
4
0.0
REGEX AND INTRODUCTION TO NLP
0.1
BASIC LEXICAL PROCESSING
0.2
MULTIPLE LINEAR REGRESSION
0.3
ADVANCED LEXICAL PROCESSING
UNSUPERVISED LEARNING: CLUSTERING
4
0.0
INTRODUCTION TO CLUSTERING
0.1
K-MEANS CLUSTERING
0.2
HIERARCHICAL CLUSTERING
0.3
OTHER FORMS OF CLUSTERING: K-MODE, K-PROTOTYPE, DB SCAN
CLASSIFICATION USING DECISION TREES
3
0.0
INTRODUCTION TO DECISION TREES
0.1
ALGORITHMS FOR DECISION TREES CONSTRUCTION
0.2
HYPERPARAMETER TUNING IN DECISION TREES
LOGISTIC REGRESSION
4
0.0
UNIVARIATE LOGISTIC REGRESSION
0.1
MULTIVARIATE LOGISTIC REGRESSION
0.2
MODEL BUILDING AND EVALUATION
0.3
LOGISTIC REGRESSION: INDUSTRY APPLICATIONS
LINEAR REGRESSION
5
0.0
SIMPLE LINEAR REGRESSION
0.1
SIMPLE LINEAR REGRESSION IN PYTHON
0.2
MULTIPLE LINEAR REGRESSION
0.3
MULTIPLE LINEAR REGRESSION IN PYTHON
0.4
INDUSTRY RELEVANCE OF LINEAR REGRESSION
DATA ANALYSIS USING SQL
6
0.0
DATABASE DESIGN
0.1
DATABASE CREATION IN MYSQL WORKBENCH QUERYING IN MYSQL
0.2
JOINS AND SET OPERATIONS
0.3
CASE STATEMENTS, STORED ROUTINES AND CURSORS
0.4
QUERY OPTIMISATION AND BEST PRACTICES
0.5
PROBLEM-SOLVING USING SQL
EXPLORATORY DATA ANALYSIS
4
0.0
DATA SOURCING
0.1
DATA CLEANING
0.2
UNIVARIATE ANALYSIS
0.3
BIVARIATE ANALYSIS AND MULTIVARIATE ANALYSIS
ANALYTICS PROBLEM SOLVING
2
1.0
THE CRISP-DM FRAMEWORK – BUSINESS AND DATA UNDERSTANDING
1.1
CRISP-DM FRAMEWORK – DATA PREPARATION, MODELLING, EVALUATION AND DEPLOYMENT
PYTHON FOR DATA SCIENCE
4
2.0
INTRODUCTION TO NUMPY
2.1
INTRODUCTION TO MATPLOTLIB
2.2
INTRODUCTION TO PANDAS
2.3
GETTING AND CLEANING DATA
DATA VISUALIZATION IN PYTHON
2
3.0
INTRODUCTION TO DATA VISUALIZATION
3.1
DATA VISUALISATION USING SEABORN
Data Science & AI
Curriculum
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