Curriculum
- 10 Sections
- 158 Lessons
- 10 Weeks
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- INTRODUCTION TO DATA SCIENCE.14
- 1.1Definition, Similarities, and Differences between Data Science and Analytics.
- 1.2Types of Data in Data Science and Analytics
- 1.3Data Preprocessing.
- 1.4Data Quality Assessment.
- 1.5Sources of Traditional and Big Data.
- 1.6Career Pathways in Data Science and Analytics.
- 1.7Data Science Explaining the Past(Descriptive Analytics)
- 1.8Predictive Analytics.
- 1.9Introduction to Artificial Intelligence.
- 1.10Introduction to Generative Artificial Intelligence and Large Language Model.
- 1.11Data Governance and Privacy
- 1.12Modern Data Management Practices.
- 1.13Programming Languages, Software and Frameworks.
- 1.14Data Science and Analytics Foundation Quiz30 Minutes20 Questions
- PYTHON ESSENTIALS FOR DATA SCIENCE37
- 2.1Introduction
- 2.2Python Installation
- 2.3Anaconda installation
- 2.4Google Colab
- 2.5Anaconda vs Google colab
- 2.6PYTHON BASICS
- 2.7Syntax and Comments
- 2.8Variables
- 2.9Data types
- 2.10Data Structures
- 2.11String manipulation and operations
- 2.12Methods in python
- 2.13Control flow statements
- 2.14F-string
- 2.15Functions in python
- 2.16Chatbot case study
- 2.17Simple Calculator3 Days
- 2.18String manipulation and operations3 Days
- 2.19Data structure assignments3 Days
- 2.20Control flow assignment3 Days
- 2.21Chatbot assignment3 Days
- 2.22DATA PREPARATION WITH NUMPY AND PANDAS
- 2.23Numpy
- 2.24Pandas
- 2.25Numpy and Pandas questions3 Days
- 2.26DATA VISUALISATION
- 2.27Components of a plot
- 2.28Data visualisation toolkit
- 2.29Matplotlib
- 2.30Matplotlib Questions
- 2.31Seaborn
- 2.32Seaborn questions
- 2.33EXPLARATORY DATA ANALYSIS
- 2.34Outliers
- 2.35EDA Process
- 2.36Titanic assignment3 Days
- 2.37Tips assignment3 Days
- DATA VISUALIZATION24
- 3.1INTRODUCTION
- 3.2POWER BI DOWNLOAD AND INSTALLATION
- 3.3DATA LOADING IN POWER BI
- 3.4POWER QUERY
- 3.5Introduction quiz10 Minutes5 Questions
- 3.6NUMERIC VISUALS & INTRODUCTION TO VISUALS
- 3.7GRAPHIC VISUALS
- 3.8SLICERS AND CUSTOM VISUALS
- 3.9INTODUCTIONS TO VISUAL STORYTELLING
- 3.10VISUAL STORYTELLING
- 3.11DASHBOARDS
- 3.12Visualizations assignment using Power BI3 Days
- 3.13TABLEAU INTRODUCTION AND INSTALLATION
- 3.14TABLEAU INTERFACE
- 3.15CONNECTING TO EXCEL DATA
- 3.16CONNECTING TO PDF
- 3.17INTODUCTION TO VISUALS IN TABLEAU
- 3.18VISUALS IN TABLEAU
- 3.19VISUALS IN TABLEAU CONT….
- 3.20FINAL VISUALS IN TABLEAU
- 3.21Tableau quiz10 Minutes10 Questions
- 3.22INTRODUCTION TO FORECASTING
- 3.23SALES FORECASTING IN TABLEAU
- 3.24Tableau Assignment
- MACHINE LEARNING ASSOCIATE (PART 1)32
- 4.1WHAT IS MACHINE LEARNING ?
- 4.2MACHINE LEARNING VS ARTIFICIAL INTELLIGENCE.
- 4.3STATISTICAL MODELLING OF MACHINE LEARNING.
- 4.4APPLICATIONS OF MACHINE LEARNING.
- 4.5POPULAR ML ALGORITHMS
- 4.6CLUSTERING
- 4.7CLASSIFICATION AND REGRESSION
- 4.8SUPERVISED VS UNSUPERVISED
- 4.9CHOICE OF ML ALGORITHMS
- 4.10REGRESSION LINE
- 4.11BEST FIT LINE
- 4.12ASSUMPTIONS OF SIMPLE LINEAR REGRESSION
- 4.13READING AND UNDERSTANDING THE DATA
- 4.14HYPOTHESIS TESTING IN LINEAR REGRESSION
- 4.15BUILDING A LINEAR MODEL
- 4.16RESIDUAL ANALYSIS AND PREDICTIONS
- 4.17LINEAR REGRESSION USING SKLEARN
- 4.18SIMPLE LINEAR REG VS MULTIPLE LINEAR REG
- 4.19MULTICOLLINEARITY
- 4.20DEALING WITH CATEGORICAL VARIABLES
- 4.21MODEL ASSESSMENT AND COMPARISON
- 4.22FEATURE SELECTION
- 4.23INTRODUCTION: UNIVARIATE LOGISTIC REGRESSION
- 4.24BINARY CLASSIFICATION
- 4.25SIGMOID CURVE
- 4.26FINDING THE BEST FIT SIGMOID CURVE SUMMARY
- 4.27MULTIVARIATE LOGISTIC REGRESSION
- 4.28DATA CLEANING AND PREPARATION
- 4.29BUILDING YOUR FIRST MODEL
- 4.30FEATURE ELIMINATION USING RFE
- 4.31CONFUSION MATRIX AND ACCURACY
- 4.32MANUAL FEATURE ELIMINATION
- MACHINE LEARNING ASSOCIATE (PART 2)LOGISTIC REGRESSION MODEL EVALUATION19
- 5.1METRICS BEYOND ACCURACY: SENSITIVITY & SPECIFICITY
- 5.2FINDING THE OPTIMAL THRESHOLD USING ROC CURVE
- 5.3METRICS BEYOND ACCURACY: PRECISION& RECALL
- 5.4INTRODUCTIONTO KNN
- 5.5HOW IT WORKS: THEORY
- 5.6PROS AND CONS OF KNN
- 5.7APPLICATIONS OF KNN
- 5.8MODEL BUILDING KNN IN PYTHON SKLEARN
- 5.9EVALUATION: KNN MODEL.
- 5.10UNSUPERVISED LEARNING: CLUSTERING (INTRODUCTION).
- 5.11UNDERSTANDING CLUSTERING
- 5.12PRACTICAL EXAMPLE OF CLUSTERING – CUSTOMER SEGMENTATION
- 5.13K MEANS CLUSTERING (INTRODUCTION) .
- 5.14PRACTICAL CONSIDERATION IN K MEANS ALGORITHM
- 5.15CLUSTER TENDENCY
- 5.16K MEANS IN PYTHON CASE: IRIS DATASET CLUSTERING
- 5.17HIERARCHICAL CLUSTERING ALGORITHM
- 5.18UNSUPERVISED LEARNING: PRINCIPLE COMPONENT ANALYSIS (PCA)
- 5.19PCA IRIS DATASET
- ADVANCED MACHINE LEARNING (PART 1)12
- 6.1INTRODUCTION
- 6.2DECISION TREE ON WINE DATASET (Python Implementation)
- 6.3CONCEPT OF HOMOGENEITY
- 6.4GINI INDEX
- 6.5ENTROPY AND INFORMATION GAIN
- 6.6SPLITTING BY R-SQUARED
- 6.7DECISION TREE HYPERPARAMETER TUNING
- 6.8TREE TRUNCATION
- 6.9RANDOM FOREST ENSEMBLE BAGGING TECHNIQUE
- 6.10BAYES THEOREM AND ALGORITHM BUILDING BLOCKS
- 6.11NAIVE BAYES: TEXT CLASSIFICATION HAM VS SPAM CASESTUDY.
- 6.12SUPPORT VECTOR MACHINE
- ADVANCED MACHINE LEARNING (PART 2)3
- QUERYING DATA IN SQL10
- STATISTICS FOR DATA SCIENCE18
- 9.1Introduction to Statistics
- 9.2Data Types in Statistics
- 9.3Qualitative data Types
- 9.4Quantitative Data Types
- 9.5Descriptive Statistics
- 9.6Measures of Central Tendency
- 9.7Measures of Variability
- 9.8Measures of asymmetry
- 9.9Covariance
- 9.10Correlation
- 9.11Linear Regression
- 9.12Probability Distribution
- 9.13Normal Distribution
- 9.14Binomial Distribution
- 9.15Poisson Distribution
- 9.16Sampling Methods
- 9.17Hypothesis Testing
- 9.18Assessment Test20 Minutes20 Questions
- BIG DATA3
Introduction to Statistics
What is statistics?
Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, and presentation of masses of numerical data. It is basically a collection of quantitative data.
Types of Statistics –
- Theoretical Statistics
- Applied Statistics
In this topic we will discuss the following:
