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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery	Frequency Of Classes
English	Self Study, Virtual Classroom	Video and Text Based	Weekends

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	SSBM

The Syllabus

Introduction to Python and IDEs – The basics of the python programming language, how you can use various IDEs for python development like Jupyter, Pycharm, etc.
Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, file handling, exception handling ,etc.
Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.
Hands-on Sessions And Assignments for Practice – The culmination of all the above concepts with real-world problem statements for better understanding.

What is Data – Introduction to data, various types of data, how data can be represented in various formats, how you can make use of unstructured data, etc.
What is Data Analytics – Introduction to data analytics, data analytics life cycle, steps involved in data analytics, importance of data analytics, how data analytics is used to gather important insights from unstructured data, real world applications of successful campaigns driven by data analytics.
What is Decision Making – Introduction to decision making, importance of decision making, components and steps involved in decision making, data driven decision making and real world applications.

Linear Algebra And Advanced Statistics

Descriptive Statistics – Measure of central tendency, measure of spread, five points summary, etc.
Probability -Probability Distributions, bayes theorem, central limit theorem.
Inferential Statistics – Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.

Python And Data Analytics

Extract Transform Load – Web Scraping, Interacting with APIs
Data Handling with NumPy
- NumPy Arrays, CRUD Operations,etc.
- Linear Algebra – Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, Matrices.
Data Manipulation Using Pandas – Loading the data, dataframes, series, CRUD operations, splitting the data, etc.
Data Preprocessing
- Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, standardization, etc.
- Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross validation techniques, train-test split, etc.
Data Visualization
- Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc, with Python matplotlib.
- Regression plots, categorical plots, area plots, etc, with Python seaborn.
Introduction to Machine learning
- Supervised – What is supervised learning, what kind of data can be used for supervised learning, supervised learning models, supervised learning lifecycle, etc.
- Unsupervised learning – What is unsupervised learning, how is it different from supervised learning, unsupervised learning models, unsupervised learning approach and lifecycle.
- Introduction to scikit-learn, Keras, etc.
Regression
- Introduction classification problems, Identification of a regression problem, dependent and independent variables.
- How to train the model in a regression problem.
- How to evaluate the model for a regression problem.
- How to optimize the efficiency of the regression model.
Classification
- Introduction to classification problems, Identification of a classification problem, dependent and independent variables.
- Training a model in a classification problem, how it is different from a regression problem, training with single and multiple features.
- Evaluating the accuracy of the classification models.
Clustering
- Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.
- How the clustering problems are different from supervised learning and unsupervised learning.
- How to train the model in a clustering problem.
- Evaluating the efficiency of the models created for clustering models.
Supervised Learning
- Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.
- Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.
- Decision Tree – Creating decision tree models on classification problems in a tree like format with optimal solutions.
- Random Forest – Creating random forest models for classification problems in a supervised learning approach.
- Support Vector Machine – SVM or support vector machines for regression and classification problems.
- Gradient Descent – Gradient descent algorithm that is an iterative optimization approach to finding local minimum and maximum of a given function.
- K-Nearest Neighbors – A simple algorithm that can be used for classification problems.
- Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting.
Unsupervised Learning
- K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach.
- Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation.
- Linear Discriminant Analysis – LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data.
- Principal Component Analysis – PCA follows the same approach in handling the multidimensional data.
Performance Metrics
- Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc.
- Confusion matrix – To evaluate the true positive/negative, false positive/negative outcomes in the model.
- r2, adjusted r2, mean squared error, etc.

Excel Fundamentals

Reading the Data, Referencing in formulas , Name Range, Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering
Working with Charts in Excel, Pivot Table, Dashboards, Data And File Security
VBA Macros, Ranges and Worksheet in VBA
IF conditions, loops, Debugging, etc.

Excel For Data Analytics

Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc.

Data Visualization with Excel

Charts, Pie charts, Scatter and bubble charts
Bar charts, Column charts, Line charts, Maps
Multiples: A set of charts with the same axes, Matrices, Cards, Tiles

Excel Power Tools

Power Pivot, Power Query and Power View

Classification Problems using Excel

Binary Classification Problems, Confusion Matrix, AUC and ROC curve
Multiple Classification Problems

Information Measure in Excel

Probability, Entropy, Dependence
Mutual Information

Regression Problems Using Excel

Standardization, Normalization, Probability Distributions
Inferential Statistics, Hypothesis Testing, ANOVA, Covariance, Correlation
Linear Regression, Logistic Regression, Error in regression, Information Gain using Regression

Visualization Basics

Introduction to Data Visualization
Various Plots for data Analysis
Determining Which Visual Representation goes best with specific data – categorical, non-categorical plots.
Finding relationships between various features in the data using visual representations.

Visualization Tools

Introduction to various visualization tools
Creating dashboards using various visualization tools.
Gathering insights from the visual representations.

Power BI Basics

Introduction to PowerBI, Use cases and BI Tools , Data Warehousing, Power BI components, Power BI Desktop, workflows and reports , Data Extraction with Power BI.
SaaS Connectors, Working with Azure SQL database, Python and R with Power BI
Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data ,M Query and Hierarchies in Power BI.