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Top Data Science Course with Machine Learning and AI Certification

Joint Co-Branded Participation Certification by Ivy Pro School & FutureSkills Prime, a Digital Skilling Initiative by MeitY and NASSCOM

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Course Details

Top Data Science Course in India with Machine Learning, AI Certification

Ivy Pro School's Data Science Course covers: data analytics, machine learning, visualization, deep learning and soft skills with a strong emphasis on hands-on learning using industry leadings tools like TensorFlow, Python, SQL, R, Tableau, VBA, Excel. The course is designed to create industry ready Data Scientists.


Course Duration – 225 hours

Online Live Program Available

Classroom Programs - Kolkata, Delhi, Pune, Bangalore, Mumbai

Every Data Scientist must essentially be an expert in Machine Learning and handling large data. The course comprises of various Machine Learning supervised and unsupervised learning algorithms practiced on tools like SQL, R, Python, Tableau, Excel & VBA

This course includes

30+

Mandatory Case-Studies to build a strong resume

10+

Industry Projects by industry partners

Mentorship by Directors & Faculties

1:1

Lifetime

Placement Assistance & Relation

Guaranteed

Internships / Industry Projects

24x7 Help

To ensure no delay in learning

Data Career with Ivy Professional School

Become Certified Data Scientist by Top Data Science Course in India since 2007.

Avail our unique and student-oriented Learning Management System facility. Challenge yourself through our various exciting industry specific projects and enrich your learning.

Flexible Payment - Loan option for students.



Additional placement assistance with CV writing practice, mock-up interviews to ensure your dream comes true.

Data Science Course Curriculum

1. Advanced Excel

Data Hygiene – Clean, structure, and prepare your dataset
Formatting – Number and table formatting for clarity
Filtering & Sorting – Auto filters, advanced filters, custom sorting
Cell Referencing – Relative, absolute references & formula handling
Basic Functions – SUM, AVERAGE, COUNT, PRODUCT, etc.
Date Functions – TODAY, DATEDIF, EOMONTH, WEEKDAY, etc.
Conditional Functions – SUMIFS, COUNTIFS, AVERAGEIFS
Database Functions – DSUM, DAVERAGE using criteria tables
Dynamic Arrays (Google Sheets) – FILTER, SORT, UNIQUE, TEXTJOIN
Pivot Tables – Layout, grouping, calculated fields, summarizing
Charts – Chart creation, types, usage, sparklines
Dashboards – Planning, business insights, slicers, summaries
Logical Functions – IF, AND, OR, IFERROR, SWITCH etc
Lookup Functions – VLOOKUP, XLOOKUP, MATCH, INDIRECT etc
Text Functions – LEFT, MID, FIND, SUBSTITUTE, CONCAT etc
Conditional Formatting – Color scales, data bars, formulas
Data Validation – Lists, error alerts, dependent dropdowns
Goal Seek & Solver – Solve optimization and reverse calculations
Gen AI - How to Use Gen AI in the Microsfor Excel, Use Lab.Generative.
Chatgpt - How to use Prompt engeerning for Data Analysis

2. SQL Queries & Relational Database Management

SQL Basics: Introduction to Database Management System (DBMS), Introduction to Google BigQuery and MySQL, CRUD Operations (Create, Read, Update, Delete)
Data Manipulation and Transformation: Logical, Numerical, and Mathematical Operators, Conditional Statements (CASE, IF, etc.)
Data Cleaning: Type Casting and Data Type Conversion, Date and Time Formatting, Text Formatting (TRIM, LOWER, UPPER, REPLACE, etc.)
Pattern Matching: LIKE Operator, REGEXP Function (Regular Expressions)
Joins and Relational Databases: Primary Key, Foreign Key, Different Types of Joins (Inner, Left, Right, Full, Self, Cross), Indexing and Performance Optimization, Database Normalization
Aggregation and Grouping: Aggregate Functions (SUM, AVG, COUNT, etc.), GROUP BY and HAVING, Pivoting and Rollup, Common Table Expressions (CTEs), User Defined Variables
Window Functions: How to use Window Functions for Advanced Aggregation (RANK, DENSE_RANK, ROW_NUMBER, LEAD, LAG, etc.)
Data Reusability: Creating and Using Views, Stored Procedures and Parameters, User Defined Functions (UDFs)
Subqueries: Simple Subqueries, EXISTS and NOT EXISTS, Correlated Subqueries
Cloud Services : Using Google BigQuery for Large-Scale Querying, Exporting and Sharing Query Results in Google BigQuery, Setting Permissions and Access Roles in Cloud Databases, Running Scheduled Queries in BigQuery
Online SQL Platforms : Connecting to Cloud Databases via MySQL Workbench, Writing and Testing SQL in Online IDEs (Mode, POPSQL, DB Fiddle, etc.), Integration with Cloud Storage (e.g., Google Cloud Storage, AWS S3)
3. Data Visualization using Tableau
Introduction to Tableau: Approaching Business Problem, Different Sections of Tableau
Connecting and Shaping Data: Connecting Data Source to Tableau, Pivoting, Calculated Field, Dimension vs. Measure
Introduction to Basic Charts
Working With Marks Card
Different Filters in Tableau
Introduction to Calculated Field: Summarization Function, String Manipulation Function, Date Functions, Logical Functions
Combining Tables: Joins, Unions, & Blending
Table Calculations: Primary and Secondary Calculations
Parameters: Dimension, Measure, Sort, TopN, Date
Groups & Sets
Analytics: Forecasting, Trend Line, Clustering
Dashboard Building & Actions: Filter, Highlight Go to, Set, Parameters
LODs: Include, Exclude, Fixed, Table Scoped
Projects: RFM (Recency, Frequency, Monetary value) Analysis, Customer Retention Dashboard
4. Data Visualization Using Power BI
"Introduction to Power BI : Understand data visualization, Power BI interface, and approaching business problems.
"
"Connecting & Shaping Data : Use Power Query for data cleaning, Transformation, Append and Merge
"
Data Modeling & Relationships : Learn normalization, build table relationships, and understand filter flow within models.
Analyzing with DAX : Explore calculated columns, measures, DAX functions (text, math, logical, time intelligence), and iterator functions.
Visualizing Data : Build interactive reports using charts, slicers, bookmarks, drillthroughs, buttons, and analytics tools.
AI & Custom Visuals : Use smart visuals like decomposition trees, Q&A, key influencers, narratives, and custom visuals.
Data Integration & Security : Work with Power BI Service, publish reports, set up refresh schedules, gateways, and Row-Level Security (RLS).
Work with Power BI Service, publish reports, set up refresh schedules, gateways, and Row-Level Security (RLS).
SQL & Other Integrations : Connect Power BI with SQL databases and write custom SQL queries.
5. Business Statistics
Types of Data
Correlation
Measures of Central Tendency
Measures of Dispersion
Probability
Probability Distributions
Sampling and Estimation
Hypothesis Testing
Data Modeling
6. Predictive Modeling with R
Introduction to R - R console, Rstudio
Introduction to R Vectors,Matrices
Introduction to Programming - condition, loops and function
Introduction to R DataFrames and manipulation of data
Introduction to Advanced R Programming with packages (dplyr etc.)
Probability Distributions and Hypothesis Testing
Overview of ggplot2
Linear Regression in R (Project: Predict Academic Performance of School Students)
Logistic Regression in R (Project: Predict Customer Churn for a Telecom Firm)
Decision Trees (Project: Predict Loan Approval)
Forecasting & Time Series Analysis (Project: Sales Forecasting for Retail)
Clustering (Project: Airline Customer Segmentation)
7. Python for Data Analysis
Introduction to Python: Setting up Python, different IDEs.
Data Types - integer, float, complex, strings
Advanced Data Types - List, Tuple, Set, Dictionary
Program Control Flow - conditions, loop
Function and Numpy
Pandas DataFrame Basics: Accessing, Filtering, and Cleaning
Pandas DataFrame Operations: GroupBy, Merging, and Transformations
Exploratory Data Analysis: Univariate, Bivariate & Multivariate Analysis
Linear Regression (Project: Ad Revenue Prediction)
Logistic Regression (Project: Customer Click Prediction)
Decision Tree (Project: Used Car Price Prediction)
Forecasting & Time Series Analysis (Project: Retail Store Sales Forecasting)
Clustering (Project: Customer Profiling)
Deployment, API Integration, and GitHub for Project Sharing
8. Machine Learning, Artificial Intelligence, and Deep Learning
Recap of Linear and Logistic Regression, Decision Tree (Project: Predicting Car Prices)
Ensemble Learning - Random Forrest (Project: Loan Risk Prediction)
Boosting Algorithms - Gradient, Ada and Xgboost (Project: Predicting House Prices, Disease Diagnosis Prediction)
Support Vector Machines (Project: Youtube Video Analysis)
Naive Bayes (Project: Customer Churn Prediction)
K-Nearest Neighbors (Project: Customer Feedback Categorization)
PCA, K-Means Clustering, Hierarchical Clustering, DBSCAN (Project: Retail Customer Profiling)
Text Preprocessing and Cleaning with Regular Expressions
Text mining, wordcloud and Sentiment Analysis (project: Indigo tweets)
Text Classification using NLP and AI (Project: IT Ticket Classification)
Building and Deploying a GEN AI Model for Custom Text Classification using Transformers
Introduction to Neural Networks (Project: Predicting Customer Lifetime Value, Credit Risk Assessment)
Recurrent Neural Networks with LSTM (Project: Infosys Stock price Prediction)
Convolutional Neural Networks (Project: Face Image Classification)
9. Automation of Excel Using VBA
VBA Programming: Recording Macros, Automating Tasks
Programming Concepts: Data Types, Procedures, Conditional Logic, Loops
Analysis Using VBA: Objects, User-Defined Functions, Creating Dashboards
4 Industry Projects

Data Science Course Syllabus

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How does our certificate benefit you?

This Course by Ivy Pro School & FutureSkills Prime, a Digital Skilling Initiative by MeitY and NASSCOM is mapped to National Occupation Standards (NOS). This ensures quality and comprehensiveness of content coverage as well as ample credibility in the data science field.

Start Learning now with Real Life Case Studies

Module 1

Module 2

Module 3

Adv Excel, VBA & SQL for Data Analytics

Power BI & Tableau
for Visualization

Machine Learning & Python for Data Science

Creating Interactive Dashboards in Excel

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Introduction to Machine Learning

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Learn From Oldest Data Science, Analytics, ML & AI Course Provider in India
Ranked 3rd as Top Data Science Institute in 2024

Ivy's Top-Ranked Faculty

Raju Kumar Misra

Ex-IISc Bangalore and Ex-Oracle, a Kaggle Grandmaster, consultant, author, and a corporate trainer for Data Analytics, Machine Learning and Deep Learning. He has authored two books on Data Structures and on Big Data already, he was a proud achiever of the Motorola medal for being the best graduate student of IISc 2010

The Director of Ivy Professional School, bringing over 16+ years of data visualization, story telling, and analytics experience. Eeshani holds an MS in Civil Engineering from Texas A&M University (USA).

Eeshani Agrawal

Prakhar Gupta

B.Tech from IIT Delhi, Ex-Boston Consulting Group and a co-founder of Adorithm a state-of-the-art Artificial Intelligence and Machine Learning based startup. He has donned various hats of a business advisor, Analyst, Consultant etc. utilizing his machine learning and analytics knowledge and expertise from various global business clients.

The founder and Director of Ivy Professional School. He has 16+ years of experience in Consulting and Analytics. He meritoriously earned his Masters’ in Management of Information Systems from the esteemed Texas A&M University (USA).

Prateek Agrawal

Data Science, Analytics, ML & AI Tools

Joint Co-Branded Certification by NASSCOM & IBM

Elevate your career with an elite certification with IBM, co-branded with FutureSkills Prime— by NASSCOM. It’s a badge of excellence that assures recruiters of your industry-aligned, cutting-edge skills.


By completing this certification, you’re telling recruiters that you’re not only job-ready but possess in-demand, cutting-edge expertise in Data Science, Machine Learning, and AI.

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Eminent Teacher and lectures who have vivid industrial experience has guide us through rigorous modules of data analytics. Nice placement support and strong alumni of IVY will help to build a good career in data analytics. Best of luck to all of you.

Ramit Podder

Cognizant

Beroe Inc

Monica Nandagopal

Very professional and organized. The faculties are well versed in analytics and provide hands-on training. They provide a conceptual understanding of analytics with a lot of case studies and practical training.

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Data Science Course FAQ

1. Can I pay the data science course fee at Ivy Professional School in installments?

Data Science is a multidisciplinary field that involves extracting insights and knowledge from data through various techniques such as data analysis, machine learning, and statistical modeling. It aims to uncover patterns, trends, and correlations to make data-driven decisions and solve complex problems.

2. What are the prerequisites of doing a Data Science course?
The prerequisites for a Data Science course may vary depending on the institute. However, having a strong foundation in mathematics, statistics, and programming can be beneficial. Some courses may also require basic knowledge of databases and data manipulation.
3. What are the technical skills required to become a Data Scientist?
To become a Data Scientist, it is essential to have strong technical skills. This includes proficiency in programming languages such as Python or R, knowledge of data manipulation and analysis using tools like SQL and Pandas, expertise in machine learning algorithms, and experience with data visualization tools like Tableau or Matplotlib.
4. What are the business skills required to become a Data Scientist?
In addition to technical skills, Data Scientists can benefit from having business acumen. This includes the ability to understand and translate business problems into analytical solutions, effective communication and presentation skills, and a strategic mindset to derive actionable insights from data that drive business growth.
5. Should I learn Python for a Data Science course?
Learning Python is highly recommended for a Data Science course. Python is one of the most popular programming languages in the field of data science due to its extensive libraries, ease of use, and versatility. It provides robust support for data manipulation, analysis, and machine learning algorithms.
6. Should I learn Statistics for a Data Science course?
Yes, learning statistics is crucial for a Data Science course. Statistics forms the foundation of data analysis and helps in understanding the principles and techniques used in data modeling, hypothesis testing, and drawing meaningful conclusions from data.
7. What will you learn in a Data Science course?
In a Data Science course, you will learn a wide range of topics including data manipulation, statistical analysis, machine learning algorithms, data visualization, and big data processing. You will also gain hands-on experience with industry-leading tools and frameworks used in data science projects.
8. How to learn Data Science?
You can learn Data Science by enrolling in a reputable institute that offers Data Science courses. Look for institutes that provide comprehensive curriculum, experienced faculty, hands-on training, and industry exposure. Online resources, tutorials, and self-study can also complement your learning journey.
9. Do I need a PG degree to do a data science certification?

While a post-graduate degree can be beneficial, it is not always a mandatory requirement for data science certification. Many institutes offer certification programs that focus on practical skills and real-world applications of data science. Having a strong foundation in relevant subjects like mathematics, statistics, and programming is more important.

10. How to Become a Data Scientist?
To become a Data Scientist, you can follow these steps:
Acquire a strong foundation in mathematics, statistics, and programming.
Enroll in a reputable Data Science course or certification program.
Gain practical experience through hands-on projects and internships.
Build a portfolio showcasing your data science projects and skills.
Continuously update your knowledge by staying informed about the latest developments in the field.
Leverage networking opportunities and participate in data science communities.
11. What are the Data Science job opportunities?
There are a wide range of Data Science job opportunities across various industries. Technology companies, e-commerce platforms, financial institutions, healthcare organizations, and marketing agencies are actively seeking skilled Data Scientists. Job roles may include Data Scientist, Data Analyst, Machine Learning Engineer, Business Analyst, and Data Engineer.
12. What kind of companies hire data scientists?
Data scientists are in high demand across various industries. Companies in sectors such as technology, finance, e-commerce, healthcare actively seek data scientists to drive data-driven decisions and innovation.
13. Why choose Ivy Professional School for a Data Science Course?

Ivy Professional School stands out for its quality education, industry relevance, and comprehensive curriculum. Our institute offers experienced instructors from IITs and IIMs and practical training. Each module consists of 20+ real-world projects, and we have successfully placed more than 32500 students in 400+ companies.

14. What certifications are provided by Ivy Professional School for data science courses?

Ivy Professional School offers various certifications related to data science, such as Data Science Certification, Machine Learning Certification, Big Data Certification, and more. The specific certifications available may vary based on the courses and programs offered. We also provide our students with a certificate from NASSCOM, authorized by the Government of India, upon successful completion of our course.

15. Will I get a refund on cancellation of my enrollment at Ivy Professional School?

Yes, we offer a 15-day money-back guarantee to all our students if they are not satisfied with the learning experience with us.

16. Will I receive a certificate after completing a course at Ivy Professional School?

Yes, we provide certificates upon the successful completion of data science courses. These certificates hold value and recognition in the industry and can enhance your career prospects in the field of data science.

17. What should I do if I miss any online session of the data science course at Ivy Professional School?

In case you miss an online session, we provide all our students with recorded sessions. Additionally, you can reach out to the faculty or support team for further assistance or clarification on the missed content.

18. How about Ivy Professional School's Placement Services?
We provide lifetime placement assistance to all our students, along with resume building, interview preparation, and connecting them with relevant job opportunities in the data science industry. Our placement services aim to support students in their career advancement and job placement.
19. Can I pay the data science course fee at Ivy Professional School in installments?
We provide installment options for payment of the data science course fee. To get specific details about installment plans and payment options, it is recommended to contact our admissions department.
20. Is the Data Science certification exam fee included in the total course fee at Ivy Professional School?

Yes, the Exam fee is included with the course fee.

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