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 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 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 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 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 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 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
R Basics, background 4
PART C – R PROGRAMMING
- Comprehensive R Archive Network 30 minLecture11.1
- Demo of Installing R On windows from CRAN Website 30 minLecture11.2
- Installing R Studios on Windows OS Setting Up R Workspace. 30 minLecture11.3
- Getting Help for R-How to use help system Installing Packages – Loading And Unloading Packages 30 minLecture11.4
Getting familiar with basics 3
- Operators in R – Arithmetic, Relational, Logical and Assignment Operators 30 minLecture12.1
- Variables, Types Of Variables, Using variables Conditional statements, if else(),switch 30 minLecture12.2
- Loops: For Loops, While Loops, Using Break statement, Switch 30 minLecture12.3
The R Programming Language- Data Types 2
- Creating data objects from the keyword. How to make different type of data objects. Types of data structures in R 30 minLecture13.1
- Arrays And Lists- Create Access the elements Vectors – Create Vectors, Vectorized Operations, Power of Vectorized Operations Matrices- Building the first matrices, Matrix Operations, Sub setting, visualizing subset Data Frames- create and filter data frames, Building And Merging data frames. 30 minLecture13.2
Functions And Importing data into R 4
- Function Overview – Naming Guidelines Arguments Matching, Function with Multiple Arguments 30 minLecture14.1
- Additional Arguments using Ellipsis, Lazy Evaluation Multiple Return Values 30 minLecture14.2
- Function as Objects, Anonymous Functions Importing and exporting Data into R- importing from files like excel, csv and Minitab. 30 minLecture14.3
- Import from URL and excel Files Import from database. 30 minLecture14.4
Data Descriptive Statistics, Tabulation, Distribution 1
Graphics in R – Types of graphics 2
R – Machine Learning Libraries/ Packages 7
- Caret 30 minLecture17.1
- Missing Values – Hmisc, MICE, rForest 30 minLecture17.2
- ggplot2 30 minLecture17.3
- Regressions – lm, glm 30 minLecture17.4
- Tree based algorithms – rpart, party 30 minLecture17.5
- Tree based algorithms – rpart, party 30 minLecture17.6
- MASS & psych 30 minLecture17.7
PYTHON PANDAS 23
- Introduction 30 minLecture18.1
- Installing python pandas 30 minLecture18.2
- Python Basics: Expressions, Data Types & Variables 30 minLecture18.3
- Functions 30 minLecture18.4
- Working with Lists 30 minLecture18.5
- Working numPy arrays 30 minLecture18.6
- Working numPy arrays 30 minLecture18.7
- Reading and Writing Files 30 minLecture18.8
- Working with CSV and JSON Data 30 minLecture18.9
- Plotting with Matplotlib 30 minLecture18.10
- Scatter plot, Line plot, Bar charts, Box plot, Plotting with Pandas 30 minLecture18.11
- Working with Data Frames 30 minLecture18.12
- Aggregate functions using Pandas 30 minLecture18.13
- Data Processing, cleaning using Pandas 30 minLecture18.14
- Working with numPy , sciPy Libraries 30 minLecture18.15
- Dictionaries and Structuring Data 30 minLecture18.16
- Manipulating Strings 30 minLecture18.17
- Pattern Matching with Regular Expressions 30 minLecture18.18
- Debugging 30 minLecture18.19
- Frequency distributions 30 minLecture18.20
- Missing Values 30 minLecture18.21
- Outliers 30 minLecture18.22
- Feature Engineering 30 minLecture18.23
Python ML Libraries – Introduction 3
- Pandas 30 minLecture19.1
- sci-kit learn 30 minLecture19.2
- Seaborn, bokeh, plotly 30 minLecture19.3
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Importance of Data Science
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