INTRODUCTION TO DATA SCIENCE 4
- What is data Science? – Introduction 30 minLecture1.1
- Importance of Data Science 30 minLecture1.2
- Demand for Data Science Professional 30 minLecture1.3
- Lifecycle of data science 30 minLecture1.4
- Tools and Technologies used in data Science 30 minLecture1.5
- Business Intelligence vs Data Science vs Data Engineer 30 minLecture1.6
- Role of a data scientist 30 minLecture1.7
Fundamentals of Math and Probability 6
INTRODUCTION TO STATISTICS
- Basic understanding of linear algebra, Matrices, vectors 30 minLecture2.1
- Addition and Multiplication of matrices 30 minLecture2.2
- Fundamentals of Probability 30 minLecture2.3
- Probability distributed function and cumulative distributed function 30 minLecture2.4
- Conditional Probability 30 minLecture2.5
- Class Hand-on – Problem solving using R for vector manipulation Problem solving for probability assignments Copy Copy 30 minLecture2.6
Descriptive Statistics 4
- Describe or summaries a set of data Measure of central tendency and measure of dispersion. 30 minLecture3.1
- The mean, median, mode, Standard deviation, Variance, Range, kurtosis and skewness. 30 minLecture3.2
- Histograms, Bar chart, Box plot 30 minLecture3.3
- Class Hands-on- 5 Point summary Box Plot, Histogram and Bar Chart Exploratory analytics R Methods Copy Copy 30 minLecture3.4
Inferential Statistics 17
- What is inferential statistics Different types of Sampling techniques Central Limit Theorem 30 minLecture4.1
- Univariate & Bivariate Analysis 30 minLecture4.2
- Correlations 30 minLecture4.3
- Least Square Regression 30 minLecture4.4
- Normal Distribution 30 minLecture4.5
- Binomial Distribution & Quincunx 30 minLecture4.6
- Point estimate and Interval estimate 30 minLecture4.7
- Creating confidence interval for population parameter Characteristics of Z- distribution and T-Distribution Basics of Hypothesis Testing Copy Copy 30 minLecture4.8
- Bias & Variance trade-offs 30 minLecture4.9
- Type of test and rejection region 30 minLecture4.10
- Type of errors in Hypothesis resting, Type-l error and Type-ll errors 30 minLecture4.11
- False Positive & False Negative 30 minLecture4.12
- P-Value and Z-Score Method 30 minLecture4.13
- T-Test, Analysis of variance(ANOVA) and Analysis of Co variance(ANCOVA) 30 minLecture4.14
- Regression analysis in ANOVA 30 minLecture4.15
- Problem solving for C.L.T Problem solving Hypothesis Testing Problem solving for T-test, Z-score test Copy Copy 30 minLecture4.16
- Case study and model run for ANOVA, ANCOVA 30 minLecture4.17
Hypothesis Testing 6
- Hypothesis Testing Basics 30 minLecture5.1
- Null Hypothesis 30 minLecture5.2
- Alternate Hypothesis 30 minLecture5.3
- p-Value 30 minLecture5.4
- Type of test and Rejection Region 30 minLecture5.5
- Type o errors-Type 1 Errors, Type 2 Errors P value method, Z score Method 30 minLecture5.6
Introduction to Machine Learning 6
UNDERSTANDING AND IMPLEMENTING MACHINE LEARNING
- What is Machine Learning? 30 minLecture6.1
- Introduction to Supervised Learning, Unsupervised Learning & Semi-supervised Learning 30 minLecture6.2
- What is Reinforcement Learning? 30 minLecture6.3
- Variable Identification 30 minLecture6.4
- Missing values treatment 30 minLecture6.5
- Outliers treatment 30 minLecture6.6
Linear Regression 6
- Introduction to Linear Regression Linear Regression with Multiple Variables 30 minLecture7.1
- Disadvantage of Linear Models Interpretation of Model Outputs Understanding 30 minLecture7.2
- Multi-colinearity 30 minLecture7.3
- Missing & Outlier treatment 30 minLecture7.4
- Understanding Heteroscedasticity 30 minLecture7.5
- Case Study – Application of Linear Regression for CTG data 30 minLecture7.6
Logistic Regression 7
- Introduction to Logistic Regression 30 minLecture8.1
- Binary Logistic Regression 30 minLecture8.2
- Multinomial Logistic Regression 30 minLecture8.3
- Introduce the notion of classification Cost function for logistic regressio 30 minLecture8.4
- Application of logistic regression to multi-class classification. 30 minLecture8.5
- Confusion Matrix, Odd's Ratio and ROC Curve Advantages and Disadvantages of Logistic Regression Copy Copy 30 minLecture8.6
- AIC & BIC 30 minLecture8.7
Decision Trees 10
- Decision Tree – C4.5, CART, CHAID 30 minLecture9.1
- How to build decision tree? Understanding CART Model Classification Rules 30 minLecture9.2
- Overfitting Problem Stopping Criteria And Pruning 30 minLecture9.3
- Underfitting 30 minLecture9.4
- Gini Index 30 minLecture9.5
- Informations Gain 30 minLecture9.6
- How to find final size of Trees? Model A decision Tree. 30 minLecture9.7
- MDS Copy 30 minLecture9.8
- Random Forests and Support Vector Machines Interpretation of Model Outputs 30 minLecture9.9
- Case Study – 1 Business Case Study for Kart Model 30 minLecture9.10
Unsupervised Learning 10
- Feature Selection & Feature Extraction 30 minLecture10.1
- Feature Construction 30 minLecture10.2
- Hierarchical Clustering 30 minLecture10.3
- K-Means algorithm for clustering – groupings of unlabeled data points. 30 minLecture10.4
- Principal Component Analysis(PCA) 30 minLecture10.5
- Anomaly Detection 30 minLecture10.6
- Association rules 30 minLecture10.7
- Market Basket Analysis 30 minLecture10.8
- Customer Segmentation 30 minLecture10.9
- Dimensionality reduction on CTG 30 minLecture10.10
1. Python Introduction (PART C – PYTHON PROGRAMMING) 3
- Python background, featuresLecture11.1
- Installation and Various Python IDEsLecture11.2
- Python vs Other languagesLecture11.3
2. Basics 6
- Operators in Python – Arithmetic, Relational, Logical and Assignment OperatorsLecture12.1
- Variables, Types Of VariablesLecture12.2
- Naming conventions• Naming conventionsLecture12.3
- String operationsLecture12.4
- Conditional statements:Lecture12.5
- Loops i) For Loops ii) While Loops iii) Break, Continue, Pass, SwitchLecture12.6
3. Data Structures 5
- ListsLecture13.1
- TuplesLecture13.2
- SetsLecture13.3
- DictionariesLecture13.4
- ComprehensionsLecture13.5
4. Functions and Modules 6
- Importing a module and setting pathLecture14.1
- Creating user defined modulesLecture14.2
- Overview of standard python modulesLecture14.3
- Function Overview – Defining a function and syntaxLecture14.4
- Calling a function and passing variablesLecture14.5
- Variables scope, call by value and call by referenceLecture14.6
Functional programming 4
- MapLecture15.1
- ReduceLecture15.2
- FilterLecture15.3
- Lambda functionsLecture15.4
6. File Handling and external integrations 5
- File operationsLecture16.1
- Exception handling using try, except, finallyLecture16.2
- Database accessLecture16.3
- Web scrappingLecture16.4
- REST API callsLecture16.5
7. Python for Data Science 6
- • Numerical PythonLecture17.1
- a) nd array b) Subset, slicing c) Indexing d) List vs nd array e) Manipulating arrays f) Mathematical operations and apply functions g) Linear algebra operations CopyLecture17.2
- • PandasLecture17.3
- a) Data loading b) Series and Data frame c) Selecting rows and columns d) Position and label-based indexing e) Slicing and dicing f) Merging and concatenating g) Grouping and summarizing h) Lambda functions and pivot tables i) Data Processing, cleaning j) Missing Values k) Outliers CopyLecture17.4
- • Data visualizationLecture17.5
- a) Introduction to Matplotlib Basic plotting Figures and sub plotting Box plot, Histograms, Scatter plots, image loading b) Introduction to Seaborn Histogram, rugged plot, hex plot and density plot Joint plot, pair plot, count plot, Heatmaps c) Plotting categorical data and aggregation of values d) Plotting Time-Series data using tsplot CopyLecture17.6
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