• Data Types
• Statistical parameters, variance, standard deviation, range
• Categorical and Quantitative Data
• Descriptive Statistics
• Statistical Inference
• Sampling and Sampling Distributions
• Correlation, Covariance and Causation
• Central Limit Theorem
• Confidence Interval
• Hypothesis Testing and Error Types
• t-test and types of t-test
• Analysis of Variance (ANOVA)
• Introduction to Probability
• Probability Distributions
• Bernoulli, Uniform, Binomial, Normal Distribution
• Poisson and Exponential Distribution
• Skew Normal Distribution
• Z-Score

• What is EDA and its Importance
• Statistical approach (Data Collection, Descriptive statistics, Data Mining)
• Importing the Data
• Data Frames
• Variables, Transformation
• Standardization and Normalization
• Validation and Interpretation
• Distributions
• Histograms, Outliers
• Summarizing Distributions
• Graphs
• Bar Charts
• Box-whisker plot
• Scatter plot
• Pie Charts
• Bubble Charts

• Why R and importance of R in Analytics
• Installation of R and R-studio
• Working Directories
• Data Types
• Operators
• Loops-For and While
• If-else statements, Nested statements
• Objects, and Vectors
• Strings
• Arrays
• Lists
• Factors
• Data Frames
• Pipe Operator
• Functions (Predefined and User defined) apply, l-apply, s-apply, m-apply, t-apply, v-apply Subset/filter, which, sample, match, sort, mutate, grep, summary,gsub, select, groupby, gather, separate, Posixct
• Joins (Inner, Outer, Left, Right, Semi, Anti) in Data Frames.
• Univariate Analysis
• Dplyr, Lubridate, Tibble

• Introduction to SQL and Data bases
• SQL developer installation
• Data types
• Data types and Operators
• Create and Drop data base
• DDL,DML, DCL ,TCL, Sorting commands and other keywords
• Advanced SQL-Wild cards, Constraints, Joins, Unions, NULL, Alias, Truncate, Views, Sub queries
• Exam

• What is Python?
• Importance of Python in Data Science
• Python Installation Guidelines (Anaconda Navigator)

Python Fundamentals

• Keywords
• Built-in functions
• String Formatting
• Indexing
• Slicing
• Sequences
• Error handling in Python (try, catch, finally)
• Ignoring Warnings
• User-defined functions
• Nested functions
• Lambda, zip and map

Local and global variables

• If-else statements, Nested statements
• Loops, Nested Loops, For, While loop
• Performance measurement of loops
• Loop control statements
• Continue, Break, Pass
• Class, Constructor and methods

Python Data Structures

• Lists, Lists Comprehensions
• Sets
• Tuple
• Dictionary
• Importance of each type

Data Handling with Python

• Introduction to NumPy, Pandas
• Arrays and Matrix
• Importing and exporting datasets in Python
• Creating Data Frames
• Data Manipulations
• Scikit-Learn libraries
• Data Visualizations in Python
• Matplotlib, Seaborn, and GGplot
• Feature Engineering: Feature Selection and Extraction
• Model Selection
• Training, Testing and K-Fold cross validation

• Machine Learning
  • Types of Machine Learning
  • What is Supervised, Un-Supervised and Reinforcement
  • What are the types of each learning technique
  • Algorithms used in Machine Learning techniques
  • Difference between Data Science, Machine Learning and AI

  Generalized Linear Models (GLM)

  • Introduction to generalized linear models
  • Understanding of Linear and Logistic Regression
  • Underfitting and Overfitting
  • Trade-off between Bias and Variance
  • Regularization techniques (Ridge, Lasso, Elastic-Net Regression)
  • Ordinary Least squares
  • Maximum Likelihood
  • Sigmoid Function
  • Cost Function
  • Gradient Descent
  • One-hot Encoding
  • Label Encoding
  • Model Evaluation metrics
  • Feature Engineering (Features Selection, Extraction)
  • R-Square, Adjusted R-Square, RSME
  • Confusion Matrix
  • Evaluation metrics (Precision, Recall, F-Score, Accuracy)
  • Sensitivity and Specificity
  • ROC-AUC curves
  • Assumptions of Linear Regression
  • Imbalanced Data
  • Sampling issues- Over sampling and Under sampling
  • SMOTE, ADASYN and Near Miss

  Decision trees and Random Forests

  • Introduction of Decision Tree and its applications
  • Types of Decision Tree
  • Terminologies in Decision Tree
  • Pros and Cons of Decision Tree
  • CHAID analysis
  • Root nodes Identification
  • Gini Index, Entropy, Chi-Square, Reduction in Variance
  • Solution for overfitting in Decision Tree
  • Tree pruning
  • Hyperparameter tuning
  • Random Search and Grid Search for auto selection of parameters
  • What is Bagging?
  • Introduction to Random Forest and its applications
  • Importance of Random Forest
  • Significant feature selection using Random Forest classifier

  Boosting Machines

  • Ensembling Techniques
  • Bagging vs Boosting
  • Gradient Boosting Algorithms
  • Gradient Descent in Boosting Algorithms
  • Gradient Boosting Machines, XGBoost, and AdaBoost
  • Regression and Classification boosting techniques
  • Stacking
  • Pros and Cons of boosting Machines

• Introduction to clustering
• K-Means clustering
• Elbow Method
• Hierarchical clustering
• Real time applications

Text Mining

• Introduction to Text Analytics and Text Mining
• Introduction to NLP
• Real time applications
• Extractingtext from files
• Data cleaning
• Introduction to NLTK library
• Count Vectorizer
• Understanding of Stopwords and regular expressions
• Stemming and Lemmatization
• Word Cloud
• N-grams
• Fuzzy String Matching
• Levenshtein Algorithm
• Jaro-Winkler Algorithm
• Cosine Similarity
• Named Entity Recognition (NER)