• Topics
• Data Types
• Measure Of central tendency
• Measures of Dispersion
• Graphical Techniques
• Skewness & Kurtosis
• Box Plot
• R
• R Studio
• Descriptive Stats in R
• Python (Installation and basic commands) and Libraries
• Jupyter note book
• Set up Github
• Descriptive Stats in Python
• Pandas and Matplotlib / Seaborn

• Topics
• Random Variable
• Probability
• Probility Distribution
• Normal Distribution
• SND
• Expected Value
• Sampling Funnel
• Sampling Variation
• CLT
• Confidence interval
• Assignments Session-1 (1 hr)
• Introduction to Hypothesis Testing
• Hypothesis Testing with examples
• 2 proportion test
• 2 sample t test
• Anova and Chisquare case studies

• Visualization
• Data Cleaning
• Imputation Techniques
• Scatter Plot
• Correlation analysis
• Transformations
• Normalization and Standardization

• Topics
• Principles of Regression
• Introduction to Simple Linear Regression
• Multiple Linear Regression

• Topics
• Multiple Logistic Regression
• Confusion matrix
• False Positive, False Negative
• True Positive, True Negative
• Sensitivity, Recall, Specificity, F1 score
• Receiver operating characteristics curve (ROC curve)

• Topics
• R shiny
• Streamlit

• Topics
• Supervised vs Unsupervised learning
• Data Mining Process
• Hierarchical Clustering / Agglomerative Clustering
• Measure of distance
• Numeric - Euclidean, Manhattan, Mahalanobis
• Categorical - Binary Euclidean, Simple Matching Coefficient, Jaquard’s Coefficient
• Mixed - Gower’s General Dissimilarity Coefficient
• Types of Linkages
• Single Linkage / Nearest Neighbour
• Complete Linkage / Farthest Neighbour
• Average Linkage
• Centroid Linkage
• Visualization of clustering algorithm using Dendrogram

• Topics
• PCA and tSNE
• Why dimension reduction
• Advantages of PCA
• Calculation of PCA weights
• 2D Visualization using Principal components
• Basics of Matrix algebra

• Topics
• What is Market Basket / Affinity Analysis
• Measure of association
• Support
• Confidence
• Lift Ratio
• Apriori Algorithm

• User-based collaborative filtering
• Measure of distance / similarity between users
• Driver for recommendation
• Computation reduction techniques
• Search based methods / Item to item collaborative filtering
• Vulnerability of recommender systems

• Workflow from data to deployment
• Data nuances
• Mindsets of modelling

• Topics
• Elements of Classification Tree - Root node, Child Node, Leaf Node, etc.
• Greedy algorithm
• Measure of Entropy
• Attribute selection using Information Gain
• Implementation of Decision tree using C5.0 and Sklearn libraries

• Topics
• Encoding Methods
• OHE
• Label Encoders
• Outlier detection-Isolation Fores
• Predictive power Score

• Topics
• Recurcive Feature Elimination
• PCA

• Topics
• Splitting data into train and test
• Methods of cross validation
• Accuracy methods

• Topics
• Bagging
• Boosting
• Random Forest
• XGBM
• LGBM

• Topics
• Deciding the K value
• Building a KNN model by splitting the data
• Understanding the various generalization and regulation techniques to avoid overfitting and underfitting
• Kernel tricks

• Lasso Regression
• Ridge Regression

• Topics
• Artificial Neural Network
• Biological Neuron vs Artificial Neuron
• ANN structure
• Activation function
• Network Topology
• Classification Hyperplanes
• Best fit “boundary”
• Gradient Descent
• Stochastic Gradient Descent Intro
• Back Propogation
• Intoduction to concepts of CNN

• Topics
• Sentiment Extraction
• Lexicons and Emotion Mining

• Topics
• Probability – Recap
• Bayes Rule
• Naive Bayes Classifier
• Text Classification using Naive Bayes

• Topics
• Introduction to time series data
• Steps of forecasting
• Components of time series data
• Scatter plot and Time Plot
• Lag Plot
• ACF - Auto-Correlation Function / Correlogram
• Visualization principles
• Naive forecast methods
• Errors in forecast and its metrics
• Model Based approaches
• Linear Model
• Exponential Model
• Quadratic Model
• Additive Seasonality
• Multiplicative Seasonality
• Model-Based approaches
• AR (Auto-Regressive) model for errors
• Random walk
• ARMA (Auto-Regressive Moving Average), Order p and q
• ARIMA (Auto-Regressive Integrated Moving Average), Order p, d and q
• Data-driven approach to forecasting
• Smoothing techniques
• Moving Average
• Simple Exponential Smoothing
• Holts / Double Exponential Smoothing
• Winters / HoltWinters
• De-seasoning and de-trending
• Forecasting using Python and R

Listed below are the five most popular algorithms that all data scientist should know (we cover all of these):

• Logistic Regression
• Naive Bayes
• K-Nearest Neighbours
• Support Vector Machines
• Random Forest

No! Just a basic laptop should be sufficient for most of your personal projects.

Big Data refers to the enormous amount of data with various formats (structured, unstructured, semi-structured) generated from a variety of data sources or channels.

Data Analysis is the process of collecting and organizing raw data with the purpose to extract helpful information from it.

Data Science is a blend of various tools, algorithms, and machine learning principles for gaining useful insights from raw data. It involves designing and constructing data modelling and other data-centered operations such as preprocessing, data cleaning, analysis, etc.

Here are a few datasets sources you can rely on:

• Kaggle
• Socrata
• Non-profit research group websites

This data science course is the most comprehensive, relevant, and contemporary, meeting all the present demands of the Data Industry. Don’t expect it to be some repurposed or repackaged content of redundant archaic course materials.

What’s more is that we continually upgrade the content of this course with the changes in technology, trends, and demands to provide you the best learning resource.