Curriculum
21 Sections
74 Lessons
24 Weeks
Expand all sections
Collapse all sections
HYPOTHESIS TESTING
5
1.1
CONCEPTS OF HYPOTHESIS TESTING
1.2
I: NULL AND ALTERNATE HYPOTHESIS, MAKING A DECISION, AND CRITICAL VALUE METHOD
1.3
CONCEPTS OF HYPOTHESIS TESTING – II: P VALUE METHOD AND TYPES OF ERRORS
1.4
INDUSTRY DEMONSTRATION OF HYPOTHESIS TESTING
1.5
TWO-SAMPLE MEAN AND PROPORTION TEST, A/B TESTING
MODEL SELCTION CASE STUDY
3
2.1
PROBLEM STATEMENT
2.2
FINAL SUBMISSION
2.3
SOLUTION
TIME SERIES FORECASTING
4
3.1
INTRODUCTION TO TIME SERIES AND ITS COMPONENTS
3.2
SMOOTHING TECHNIQUES
3.3
INTRODUCTION TO AR MODELS
3.4
BUILDING AR MODELS
MODEL SELECTION & GENERAL ML TECHNIQUES
3
4.1
PRINCIPLES OF MODEL SELECTION
4.2
MODEL EVALUATION
4.3
MODEL SELECTION: BEST PRACTICES
ADVANCED ML CASE STUDY
4
5.1
PROBLEM STATEMENT
5.2
EVALUATION RUBRIC
5.3
FINAL SUBMISSION
5.4
SOLUTION
ADVANCED REGRESSION
2
6.1
GENERALISED LINEAR REGRESSION
6.2
REGULARISED REGRESSION
PRINCIPAL COMPONENT ANALYSIS
2
7.1
PRINCIPAL COMPONENT ANALYSIS AND SINGULAR VALUE DECOMPOSITION
7.2
PRINCIPAL COMPONENT ANALYSIS IN PYTHON
BOOSTING
2
8.1
INTRODUCTION TO BOOSTING AND ADABOOST
8.2
GRADIENT BOOSTING
BAGGING & RANDOM FOREST
5
9.1
POPULAR ENSEMBLES
9.2
INTRODUCTION TO RANDOM FORESTS
9.3
HIERARCHICAL CLUSTERING
9.4
FEATURE IMPORTANCE IN RANDOM FORESTS
9.5
RANDOM FORESTS IN PYTHON
CASE STUDY: LEAD SCORING
4
10.1
PROBLEM STATEMENT
10.2
EVALUATION RUBRIC
10.3
FINAL SUBMISSION
10.4
SOLUTION
BUSINESS PROBLEM SOLVING
2
11.1
INTRODUCTION TO BUSINESS PROBLEM SOLVING
11.2
BUSINESS PROBLEM SOLVING: CASE STUDY DEMONSTRATIONS
BASICS OF NLP AND TEXT MINING
4
12.1
REGEX AND INTRODUCTION TO NLP
12.2
BASIC LEXICAL PROCESSING
12.3
MULTIPLE LINEAR REGRESSION
12.4
ADVANCED LEXICAL PROCESSING
UNSUPERVISED LEARNING: CLUSTERING
4
13.1
INTRODUCTION TO CLUSTERING
13.2
K-MEANS CLUSTERING
13.3
HIERARCHICAL CLUSTERING
13.4
OTHER FORMS OF CLUSTERING: K-MODE, K-PROTOTYPE, DB SCAN
CLASSIFICATION USING DECISION TREES
3
14.1
INTRODUCTION TO DECISION TREES
14.2
ALGORITHMS FOR DECISION TREES CONSTRUCTION
14.3
HYPERPARAMETER TUNING IN DECISION TREES
LOGISTIC REGRESSION
4
15.1
UNIVARIATE LOGISTIC REGRESSION
15.2
MULTIVARIATE LOGISTIC REGRESSION
15.3
MODEL BUILDING AND EVALUATION
15.4
LOGISTIC REGRESSION: INDUSTRY APPLICATIONS
LINEAR REGRESSION
5
16.1
SIMPLE LINEAR REGRESSION
16.2
SIMPLE LINEAR REGRESSION IN PYTHON
16.3
MULTIPLE LINEAR REGRESSION
16.4
MULTIPLE LINEAR REGRESSION IN PYTHON
16.5
INDUSTRY RELEVANCE OF LINEAR REGRESSION
DATA ANALYSIS USING SQL
6
17.1
DATABASE DESIGN
17.2
DATABASE CREATION IN MYSQL WORKBENCH QUERYING IN MYSQL
17.3
JOINS AND SET OPERATIONS
17.4
CASE STATEMENTS, STORED ROUTINES AND CURSORS
17.5
QUERY OPTIMISATION AND BEST PRACTICES
17.6
PROBLEM-SOLVING USING SQL
EXPLORATORY DATA ANALYSIS
4
18.1
DATA SOURCING
18.2
DATA CLEANING
18.3
UNIVARIATE ANALYSIS
18.4
BIVARIATE ANALYSIS AND MULTIVARIATE ANALYSIS
ANALYTICS PROBLEM SOLVING
2
19.1
THE CRISP-DM FRAMEWORK – BUSINESS AND DATA UNDERSTANDING
19.2
CRISP-DM FRAMEWORK – DATA PREPARATION, MODELLING, EVALUATION AND DEPLOYMENT
PYTHON FOR DATA SCIENCE
4
20.1
INTRODUCTION TO NUMPY
20.2
INTRODUCTION TO MATPLOTLIB
20.3
INTRODUCTION TO PANDAS
20.4
GETTING AND CLEANING DATA
DATA VISUALIZATION IN PYTHON
2
21.1
INTRODUCTION TO DATA VISUALIZATION
21.2
DATA VISUALISATION USING SEABORN
Data Science & AI
Curriculum
This content is protected, please
login
and enroll in the course to view this content!
Your Website
Modal title
Main Content
