PG Program in Data Science
India’s top ranked program | 6 Months | Online Mode
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Data Science Course Description
IIBM Institute Data Science certification course introduces you to the ever-expanding world of Data and the complex and intricate techniques that are used to store, retrieve, assess & extract tangible and profitable insights from to stay ahead of the curve of uncertainty. Learn Data Science and become rich in the all-important currency of modern times, Data.
Data Scientist Skills
- Data Science- Data Analysis, Machine Learning Algorithms, Time- series analysis, K-means Clustering, Naiive Bayes, Business analytics, etc.
- Python- Anaconda, Lambda expression, OOP, Numpy, Spicy, MatPlotlib, JSON, Packages, Functions, Web Scraping, Python parser.
- Machine Learning-Python, Algorithms, Statistics & Probability, Supervised & Unsupervised Learning, Decision Trees, Random Forests, Linear & Logistic regression, etc
- Artificial Intelligence-Convolutional Neural networks (CNN), perceptron in CNN, Tensor ow, Tensor ow code, transfer learning, graph visualization, recurrent neural networks (RNN), Deep learning, GPU in Deep Learning, Keras and TFLeam APIs, back propagation, and hyperparameters.
Key Features
- No Cost EMI Option
- Certificates From IIBM Institute of Business Management
- 150-200 Hours Video Tutorials
- 17 Capstone Projects
- 100% Job Assurance
- Resume Preparation Facility
- Job Search Facility on our Job Portal - www.jobsalert.co.in
- We Have Dedicated Placement Team
- Updated Curriculum from Industry Leaders and be Ahead of the Curve by Learning what the Industry Needs
CURRICULUM
BUSINESS ANALYTICS
MACHINE LEARNING
ARTIFICIAL INTELLIGENCE
TABLEAU
BUSINESS ANALYTICS
Fundamentals of Data Analytics
Data Analytics across Domains, What is Analytics? , Types of Analytics,
AI vs ML vs DL vs DS
Basics concepts in Statistics for Data Analytics
Introduction to statistics and Central Limit Theorem, Measures of Sprea,
Descriptive Statistics with Real Time Examples, Measuring Scales,
Inferential Statistics with Real Time Examples
Advanced concepts in Statistics for Data Analytics
Hypothesis Testing and Goodness of Fit test, Introduction to Statistical Tests, Statistical Test with Real Time Example, Analysis of Variance (ANOVA) and Analysis of Covariance (ANCOVA), Probability Theory for Data Analytics, Types of Probability Distribution
Python essential for Data Science
Python Intro, IDE and Python Packages, Python Programming: String, Tuple and Variable Declaration, Python Data Types – Dictionary, List and Set, Numpy Packages – Array Handling and Manuplation, Pandas Packages – Dataframe and loading Excel, Csv File, Matplotlib Packages – Line graph and Visualisation, Histogram, Scatter Diagram, Box Plot and Bar graph, Area Chart, Dual Axis, Array reshaping, Reverse Matrix analysis, Python – Control Structures ( IF, IF-ELSE, IF-ELIF-ELSE, WHILE and FOR LOOP), Python – Data Preparation Process, Python – Functions WITH and WITHOUT arguments, Python – File Processing and Data Collection Methods, Time Series analysis and Forcasting, Simple Predictive Analysis
Data Science with Python
Data Science with Python, Data Science Application across Multiple Domain and Business Function, Data Science Project Lifecycle, Multiple Predictive Model using Python, Python – Simple and Multiple Predictive Model in Practical, Assignment – Real Estate Predictive Analysis, Python Correlation Analysis, Data Science – Questionnare Design Process, Python Classi cation Model Building, DataScience – Experimental Design Analysis, Classi cation Technique – Discriminant Analysis, Data Science – Association Rule – Apripori Algorithm, Data Science – Building Recommendation System – MarketBased Analysis (MBA), Data Science – Attrition Analysis SolutionExecution, Data Science – Bank Loan Modelling Solution Execution -Part-1, Data Science – Bank Loan Modelling Solution Execution – Part-2, Data Architecture Design, Data Warehousing And it’s Schema Design, Image Processing and Image Extraction, Image Processing and Object Recognition, Summarisation of Data Science Algorithm (Data Science Process)
ARTIFICIAL INTELLIGENCE
AI with Python – Primer Concepts, Basic Concept of Arti cial Intelligence (AI), The Necessity of Learning AI, what is Intelligence? What is Intelligence Composed Of? What’s Involved in AI, Application of AI, Cognitive Modeling: Simulating Human Thinking Procedure, Agent & Environment
Getting Started–Why Python for AI, Features of Python, Installing Python, setting up PATH, Running Python, Script from the Commandline, Integrated Development Environment.
Machine Learning–Types of Machine Learning (ML), Most Common Machine Learning Algorithms
Data Preparation-Preprocessing the Data, Techniques for Data Preprocessing, Labeling the Data
Supervised Learning: Classifcation, Steps for Building a Classifer in Python, Building Classifer in Python, Logistic Regression, Decision Tree Classifer, Random Forest Classifer, Performance of a classifer, Class Imbalance Problem, Ensemble Techniques
Supervised Learning: Regression-Building Regressors in Python
Logic Programming, How to Solve Problems with Logic Programming, Installing Useful Packages, Examples of Logic Programming, Checking for Prime Numbers, Solving Puzzles
Unsupervised Learning: Clustering, what is Clustering? Algorithms for Clustering the Data, Measuring the Clustering Performance, Calculating Silhouette Score, Finding Nearest Neighbors, K-Nearest Neighbors Classifer
Natural Language Processing, Components of NLP, Di culties in NLU, NLP Terminology, Steps in NLP
NLTK package, Importing NLTK, Downloading NLTK’s Data, Installing Other Necessary Packages, Concept of Tokenization, Stemming, and Lemmatization, Chunking: Dividing Data into Chunks, Types of chunking, Bag of Word (BoW) Model, Concept of the Statistics, Building a Bag of Words Model in NLTK, Solving Problems, Topic Modeling: Identifying Patterns in Text Data, Algorithms for Topic Modeling
Analyzing Time Series Data, Introduction, Installing Useful Packages, Pandas: Handling, Slicing and Extracting Statistic from Time Series Data, Extracting Statistic from Time Series Data, Analyzing Sequential Data by Hidden Markov Model (HMM),Example: Analysis of Stock Market data
Speech Recognition, Building a Speech Recognizer, Visualizing Audio Signals – Reading from a File and Working on it, Characterizing the Audio Signal: Transforming to Frequency Domain, Generating Monotone Audio Signal, Feature Extraction from Speech, Recognition of Spoken Words
Heuristic Search, Concept of Heuristic Search in AI, Di erence between Uninformed and Informed Search, Real World Problem Solved by Constraint Satisfaction
Gaming, Search Algorithms, Combinational Search, Minimax Algorithm, Alpha-Beta Pruning, Negamax Algorithm, Building Bots to Play Games, A Bot to Play Last Coin Standing, A Bot to Play Tic Tac Toe
Neural Networks, What is Artificial Neural Networks (ANN),Installing Useful Packages, Building Neural Networks, Perceptron based Classifier, Single – Layer Neural Networks, Multi-Layer Neural Networks
Reinforcement Learning, Basics of Reinforcement Learning, Building Blocks: Environment and Agent, Constructing an Environment with Python, Constructing a learning agent with Python
Genetic Algorithms, What are Genetic Algorithms? How to Use GA for Optimization Problems? Installing Necessary Packages, Implementing Solutions using Genetic Algorithms
Computer Vision, Computer Vision, Computer Vision Vs Image Processing, Installing Useful Packages, Reading, Writing and Displaying an Image, Color Space Conversion, Edge Detection, Face Detection, Eye Detection
MACHINE LEARNING
Machine Learning
Machine Learning Introduction and it’s Modules, Overview of Supervised Learning Algorithm, Overview of UnSupervised Learning Algorithm, How Machine Learning helps to automate the Business Process, Real Time Application of Machine Learning
Linear Regression
Simple Linear Regression, Multiple Linear Regression, Assumptions of Linear Regression, Linear Regression Case Study, Linear Regression Project – Real Estate Model Building
Logistic Regression
Logistic Regression Concepts, Odds Ratio, Logit Function/ Sigmoid Function, Cost function for logistic regression, Application of logistic regression to multi-class classi cation, Assumption in Logistics Regression, Evaluation Matrix : Confusion Matrix, Odd’s Ratio And ROC Curve, Advantages And Disadvantages of Logistic Regression, Project Attrition and Bank Loan Modelling
ANOVA and ANCOVA
ANOVA and ANCOVA Concepts, Coding of ANOVA, Application of ANOVA and ANCOVA
Linear Discriminant Analysis
Discriminant Analysis, Statistics Associated with Discriminant Analysis, Eigen Value, Case Study with Discriminant Analysis
Naïve Bayes
Naïve Bayes Concepts, Python Execution of Naïve Bayes, Conditional Probability, Bayes Theorem, Building model using Naive Bayes, Naive Bayes Assumption, Laplace Correction, NLP with Naive Bayes
KNN
Distance as Classifer, Euclidean Distance, Manhattan Distance, KNN Basics, KNN for Regression & Classifcation
SVM
Basics of SVM, Margin Maximization, Kernel Trick, RBF / Poly / Linear
Decision Tree and Random Forest
Decision Tree Concepts, Random Forest Concepts, Decision Tree and Random Forest Coding, Decision Tree and Random Forest – Attrition Project, Decision Tree and Random Forest – Bank Loan Modelling
Factor Analysis
Eigenvalues and Eigenvectors, Orthogonal Transformation, Using PCA
Cluster Analysis
Clustering Methods, Agglomerative Clustering, Divisive Clustering, Dendogram, Basics of KMeans, Finding value of optimal K, Elbow Method, Silhouette Method
Association Rule
Apriori Algorithm, MBA – Market Basket Analysis, Multi level Association Rule, Application of Association Rule CA
Correlation
Introduction about Correlation Analysis, Construction of Correlation Matrix, Person Product Movement Correlation, Partial Correlation, Non Metric Correlation
Time Series Analysis
Time Series Analysis, Data Preparation, Stationary Data, Trends /Seasonility, ARIMA Model, SARIMA & Other Models
Attrition Project
Bank loan Modelling
Merger and Acquisition Project
LKP Project
Complete Module Revision
Course Revision
Working with Time Series-Understanding Aggregation, Granularity, and Level of DetailCreating an Area Chart & Learning About Highlighting-Maps, Scatterplots, and Your First
Dashboard
Different Types of Joins in Tableau Dashboard and Storyline Creation – Joining Data in
Tableau-Creating a Map, Working with Hierarchies-Joining and Blending Data, PLUS: Dual
Axis Charts-Creating Bins – Tree Map Chart-Customer Segmentation Dashboard-Advanced
Dashboard Interactivity-Creating a Storyline.
Tableau Basics: Your First Bar chart – The Business Challenge – Who Gets the Annual
Bonus – Connecting Tableau to a Data File – CSV File – Navigating Tableau – Creating
Calculated Fields – Adding Colors – Adding Labels and Formatting – Exporting Your
Worksheet
Time series, Aggregation, and Filters – Working with Data Extracts in Tableau – Working
with Time Series – Understanding Aggregation, Granularity, and Level of Detail –
Creating an Area Chart & Learning about Highlighting – Adding a Filter and Quick Filter –
Joining and Blending Data, PLUS: Dual Axis Charts – Understanding how LEFT, RIGHT,
INNER, and OUTER Joins Work – Joins with Duplicate Values – Joining on Multiple Fields
Tableau – Maps, Scatterplots, and Your First Dashboard – Joining Data in Tableau –
Creating a Map, Working with Hierarchies – Creating a Scatter Plot, Applying Filters to
Multiple Worksheets
8 Real Time Project using Tableau
Let’s Create our First Dashboard! – Adding an Interactive Action – Filter – Adding an
Interactive Action – Highlighting
The Showdown: Joining Data vs. Blending Data in Tableau – Data Blending in Tableau and
Dual Axis Chart – Creating Calculated Fields in a Blend (Advanced Topic) – Section Recap
Table Calculations, Advanced Dashboards, Storytelling – Downloading the Dataset and
Connecting to Tableau – Mapping: how to Set Geographical Roles – Creating Table
Calculations for Gender – Creating Bins and Distributions for Age –
Leveraging the Power of Parameters – How to Create a Tree Map Chart – Creating a
Customer Segmentation Dashboard – Advanced Dashboard Interactivity – Analyzing the
Customer Segmentation Dashboard – Creating a Storyline
Advanced Data Preparation – What Format Your Data Should Be In – Data Interpreter and
Pivot – Splitting a Column into Multiple Columns – Metadata Grid – Fixing Geographical Data
Errors in Tableau
Hierarchical Clustering Using Tableau Software and K-Mean Clustering Using Tableau
Software
Complete Course Revision using Tableau Software and Steps to execute the Real Time
Project using Tableau
Programming Language and Development Tools
Meet Our Mentor
Qualification: M.tech in Automotive Electronics, B.tech in Electronics and Communication
Qualifications: Ph.D (CSE), M.E (CSE), MCA
Qualification: Post Graduate Program in Big Data and Analytics, PG Diploma in Artificial Intelligence & Machine Learning.
MBA (Marketing & IT)
Alumni in the Industry
Program Fees
PG Program in Data Science
Rs. 25,000 + GST
Post Graduate Program Certificate from
- Certification from IIBM Institute of Business Management
- 6-Month Online Program
- 15-200 Hours Of Online Learning Content
- Receive Updated Curriculum From Industry Leaders And Be Ahead Of The Curve By Learning What the Industry Needs
- Get Assistance In Creating A World Class Resume From Our Carrer Services Team.
- Individual Doubt-Solving With Expert Mentors
- 17 real-world projects guided by industry experts
- 100% Job Assurance