Curriculum
10 Sections
158 Lessons
10 Weeks
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INTRODUCTION TO DATA SCIENCE.
14
1.1
Definition, Similarities, and Differences between Data Science and Analytics.
1.2
Types of Data in Data Science and Analytics
1.3
Data Preprocessing.
1.4
Data Quality Assessment.
1.5
Sources of Traditional and Big Data.
1.6
Career Pathways in Data Science and Analytics.
1.7
Data Science Explaining the Past(Descriptive Analytics)
1.8
Predictive Analytics.
1.9
Introduction to Artificial Intelligence.
1.10
Introduction to Generative Artificial Intelligence and Large Language Model.
1.11
Data Governance and Privacy
1.12
Modern Data Management Practices.
1.13
Programming Languages, Software and Frameworks.
1.14
Data Science and Analytics Foundation Quiz
30 Minutes
20 Questions
PYTHON ESSENTIALS FOR DATA SCIENCE
37
2.1
Introduction
2.2
Python Installation
2.3
Anaconda installation
2.4
Google Colab
2.5
Anaconda vs Google colab
2.6
PYTHON BASICS
2.7
Syntax and Comments
2.8
Variables
2.9
Data types
2.10
Data Structures
2.11
String manipulation and operations
2.12
Methods in python
2.13
Control flow statements
2.14
F-string
2.15
Functions in python
2.16
Chatbot case study
2.17
Simple Calculator
3 Days
2.18
String manipulation and operations
3 Days
2.19
Data structure assignments
3 Days
2.20
Control flow assignment
3 Days
2.21
Chatbot assignment
3 Days
2.22
DATA PREPARATION WITH NUMPY AND PANDAS
2.23
Numpy
2.24
Pandas
2.25
Numpy and Pandas questions
3 Days
2.26
DATA VISUALISATION
2.27
Components of a plot
2.28
Data visualisation toolkit
2.29
Matplotlib
2.30
Matplotlib Questions
2.31
Seaborn
2.32
Seaborn questions
2.33
EXPLARATORY DATA ANALYSIS
2.34
Outliers
2.35
EDA Process
2.36
Titanic assignment
3 Days
2.37
Tips assignment
3 Days
DATA VISUALIZATION
24
3.1
INTRODUCTION
3.2
POWER BI DOWNLOAD AND INSTALLATION
3.3
DATA LOADING IN POWER BI
3.4
POWER QUERY
3.5
Introduction quiz
10 Minutes
5 Questions
3.6
NUMERIC VISUALS & INTRODUCTION TO VISUALS
3.7
GRAPHIC VISUALS
3.8
SLICERS AND CUSTOM VISUALS
3.9
INTODUCTIONS TO VISUAL STORYTELLING
3.10
VISUAL STORYTELLING
3.11
DASHBOARDS
3.12
Visualizations assignment using Power BI
3 Days
3.13
TABLEAU INTRODUCTION AND INSTALLATION
3.14
TABLEAU INTERFACE
3.15
CONNECTING TO EXCEL DATA
3.16
CONNECTING TO PDF
3.17
INTODUCTION TO VISUALS IN TABLEAU
3.18
VISUALS IN TABLEAU
3.19
VISUALS IN TABLEAU CONT….
3.20
FINAL VISUALS IN TABLEAU
3.21
Tableau quiz
10 Minutes
10 Questions
3.22
INTRODUCTION TO FORECASTING
3.23
SALES FORECASTING IN TABLEAU
3.24
Tableau Assignment
MACHINE LEARNING ASSOCIATE (PART 1)
32
4.1
WHAT IS MACHINE LEARNING ?
4.2
MACHINE LEARNING VS ARTIFICIAL INTELLIGENCE.
4.3
STATISTICAL MODELLING OF MACHINE LEARNING.
4.4
APPLICATIONS OF MACHINE LEARNING.
4.5
POPULAR ML ALGORITHMS
4.6
CLUSTERING
4.7
CLASSIFICATION AND REGRESSION
4.8
SUPERVISED VS UNSUPERVISED
4.9
CHOICE OF ML ALGORITHMS
4.10
REGRESSION LINE
4.11
BEST FIT LINE
4.12
ASSUMPTIONS OF SIMPLE LINEAR REGRESSION
4.13
READING AND UNDERSTANDING THE DATA
4.14
HYPOTHESIS TESTING IN LINEAR REGRESSION
4.15
BUILDING A LINEAR MODEL
4.16
RESIDUAL ANALYSIS AND PREDICTIONS
4.17
LINEAR REGRESSION USING SKLEARN
4.18
SIMPLE LINEAR REG VS MULTIPLE LINEAR REG
4.19
MULTICOLLINEARITY
4.20
DEALING WITH CATEGORICAL VARIABLES
4.21
MODEL ASSESSMENT AND COMPARISON
4.22
FEATURE SELECTION
4.23
INTRODUCTION: UNIVARIATE LOGISTIC REGRESSION
4.24
BINARY CLASSIFICATION
4.25
SIGMOID CURVE
4.26
FINDING THE BEST FIT SIGMOID CURVE SUMMARY
4.27
MULTIVARIATE LOGISTIC REGRESSION
4.28
DATA CLEANING AND PREPARATION
4.29
BUILDING YOUR FIRST MODEL
4.30
FEATURE ELIMINATION USING RFE
4.31
CONFUSION MATRIX AND ACCURACY
4.32
MANUAL FEATURE ELIMINATION
MACHINE LEARNING ASSOCIATE (PART 2)
LOGISTIC REGRESSION MODEL EVALUATION
19
5.1
METRICS BEYOND ACCURACY: SENSITIVITY & SPECIFICITY
5.2
FINDING THE OPTIMAL THRESHOLD USING ROC CURVE
5.3
METRICS BEYOND ACCURACY: PRECISION& RECALL
5.4
INTRODUCTIONTO KNN
5.5
HOW IT WORKS: THEORY
5.6
PROS AND CONS OF KNN
5.7
APPLICATIONS OF KNN
5.8
MODEL BUILDING KNN IN PYTHON SKLEARN
5.9
EVALUATION: KNN MODEL.
5.10
UNSUPERVISED LEARNING: CLUSTERING (INTRODUCTION).
5.11
UNDERSTANDING CLUSTERING
5.12
PRACTICAL EXAMPLE OF CLUSTERING – CUSTOMER SEGMENTATION
5.13
K MEANS CLUSTERING (INTRODUCTION) .
5.14
PRACTICAL CONSIDERATION IN K MEANS ALGORITHM
5.15
CLUSTER TENDENCY
5.16
K MEANS IN PYTHON CASE: IRIS DATASET CLUSTERING
5.17
HIERARCHICAL CLUSTERING ALGORITHM
5.18
UNSUPERVISED LEARNING: PRINCIPLE COMPONENT ANALYSIS (PCA)
5.19
PCA IRIS DATASET
ADVANCED MACHINE LEARNING (PART 1)
12
6.1
INTRODUCTION
6.2
DECISION TREE ON WINE DATASET (Python Implementation)
6.3
CONCEPT OF HOMOGENEITY
6.4
GINI INDEX
6.5
ENTROPY AND INFORMATION GAIN
6.6
SPLITTING BY R-SQUARED
6.7
DECISION TREE HYPERPARAMETER TUNING
6.8
TREE TRUNCATION
6.9
RANDOM FOREST ENSEMBLE BAGGING TECHNIQUE
6.10
BAYES THEOREM AND ALGORITHM BUILDING BLOCKS
6.11
NAIVE BAYES: TEXT CLASSIFICATION HAM VS SPAM CASESTUDY.
6.12
SUPPORT VECTOR MACHINE
ADVANCED MACHINE LEARNING (PART 2)
3
7.1
BOOSTING: INTRODUCTION, ADABOOST, GRADIENT BOOSTING, XGBOOST
7.2
ARTIFICIAL NEURAL NETWORKS.
7.3
ADVANCED ML CONCEPTS.
QUERYING DATA IN SQL
10
8.1
INTRODUCTION SQL
8.2
SQL DOWNLOAD AND INSTALLATION
8.3
SQL BASICS
8.4
STRUCTURE QUERY LANGUAGE
8.5
KEY CONSTRAINTS
8.6
SELECT WITH WHERE CLAUSE
8.7
OPERATORS WITH WHERE CLAUSE
8.8
ADVANCED SQL QUERIES
8.9
SUBQUERY & JOINS IN SQL
8.10
Quiz
10 Minutes
5 Questions
STATISTICS FOR DATA SCIENCE
18
9.1
Introduction to Statistics
9.2
Data Types in Statistics
9.3
Qualitative data Types
9.4
Quantitative Data Types
9.5
Descriptive Statistics
9.6
Measures of Central Tendency
9.7
Measures of Variability
9.8
Measures of asymmetry
9.9
Covariance
9.10
Correlation
9.11
Linear Regression
9.12
Probability Distribution
9.13
Normal Distribution
9.14
Binomial Distribution
9.15
Poisson Distribution
9.16
Sampling Methods
9.17
Hypothesis Testing
9.18
Assessment Test
20 Minutes
20 Questions
BIG DATA
3
10.1
INTRODUCTION TO BIG DATA
10.2
HADOOP VS SPARK
10.3
MANAGING BIG DATA IN DATA SCIENCE PROJECTS.
Data Science (IABAC Certification).
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