(623 Ratings)Join the #1 Ranked Data Science Course in India, Led by Industry Experts from Top Tech Companies and Transform Your Career Today!
1. Course Highlights
115+ Live Interactive Sessions
240+ Assignments & 15+ Real-World Projects
Live Doubt Clearing & Mentorship Sessions
Job Assistance with 500+ Hiring Partners
Access Recordings Anytime with Life time Access
60+ Practice & Aptitude Tests
460+ Self-Paced Learning Resources
No Cost EMI Options Available
Organizations
-Logo.wine.svg){kind=logo}
-Logo.wine.svg){kind=logo}
2. Students Testimony
Career Upgrade
Anjani Kumar
Machine Learning Engineer, SDE1, Amazon
As a Machine Learning enthusiast, I highly recommend the Logicmojo data science course. The expert trainers deliver amazing lectures and are always available to resolve any technical queries. Thanks to Logicmojo's live preparation training, I successfully cracked interviews at Zynga and Amazon.
Career Transition
Praveen Kumar
Data Scientist | Generative Ai (GenAI) Developer at RevealIT Solutions
I highly recommend Logicmojo's data science course for its outstanding lectures and the expert team's prompt support in addressing technical queries. This course helps me in securing my Data Scientist role, especially in GenAI Development. Advance your career with top-tier training and dedicated mentorship.
Career Gap
Aman Lateef
GenAI Developer at Infosys
I liked Logicmojo's data science course for its amazing lectures and the always-helpful from expert team anytime, which really helped me land a job as a Data Scientist in Invent Health Inc. It's a best data science course currently available online with best quality.
Career Gap
Ashish Anand
Data Scientist
I'm happy to share my experience with the Logicmojo Data Science program. it was a rewarding 7-month journey. The instructor covered Advanced Python, Machine Learning, Deep Learning, and Computer Vision in the classes. I built a strong profile as a Data Scientist and completed 5 projects during the classes. Thank you, team.
Career Transition
Asha Yadav
Business Analyst at Amazon
I was part of the September batch in the data science course. Regular classes were held, and the instructor explained complex topics very calmly. Solving assignments and attending classes regularly helped me to land a role as a Business Analyst at Amazon. Thanks to the Logicmojo Team
Career Launch
Himanshu Mittal
Data Scientist at Ripik.AI
I am grateful for the incredible opportunity to pursue my Data Science and AI course on the Logicmojo platform.The course provided extensive knowledge through real-time examples and practical applications, which greatly enriched my learning experience. The trainer's approach was friendly and supportive, encouraging us to ask questions and clearing doubts effectively
Career Gap
Vamsi Karuturi
Senior Lead at WalmartLabs
The best part about the course is that the instructors are amazing. You can ask any doubts at any time. Being a software engineer, I always had queries about machine learning and logic development. The trainer is easily approachable. Regular classes with coding tests help evaluate your preparation.
Career Transition
Vignesh
Data Analyst & Data Engineer at EXL
I had a great experience with Logicmojo as part of the January batch for the Data Science course. The instructor explained the concepts very well using multiple datasets from Kaggle. This approach provided a practical understanding of how to analyze millions of data points and create models using Machine Learning algorithms.
Career Launch
Siddharth Pande
Senior Software Engineer at Microsoft
One of the best resources for live training classes for data science. In-depth concepts are covered across all topics, with each explained thoroughly and complemented by projects at the end. The Logicmojo course helped me multiple times during interview preparation for companies like Walmart, Oracle, and Microsoft. There's no need for any other online materials
Career Transition
Mohammed Shirhaan
Software Engineer III
For Learning and Growing in your Tech Carrier in Data science, I Would Highly Recommend to Attend the 7 Months Program, it Help you in Very Way to Achieve your Dreams. Very Structured Course, Covering Almost all Techniques and Concepts of Data Science and Machine Learning.
Career Upgrade
Amith Kumar
ML/AI/NLP Enthusiasts at Halliburton
The Data Science course curriculum is of the highest quality, coupled with an exceptional learning experience from my tutor. It's the best course to prepare for data scientist roles with top product companies.
Career Upgrade
Rajnish Kumar
Director at Cred
Great course! Definitely helped me open some new doors in understanding how Machine Learning work and implementing solutions for the different exercises & assignment. Live Courses are recommended for Working Professionals.
Career Launch
Gunjan Yadu
Developerat BNY Mellon
The mentors are amazing. I learned the concepts of Analytics, and the best part was the focus on the minute details of the concepts in ML, which are often overlooked but essential for deep understanding. I now feel confident to switch my role.
Career Launch
K Aniket Prusty
Associate Software Engineer @ Aster DM Healthcare
Project-based learning is the best part of the Logicmojo Data Science Course. The training teaches every concept of the Data Science using a practical approach. It built my understanding of data science development so well that I was able to work on some open-source projects too. I joined their batch in January of this year. Thanks, Team.
Career Launch
Saksham Agarwal
Application Development Analyst at Accenture
I have enrolled in the Data Science Course with placement assistance at Logicmojo, and so far, everything is good. The course structure is well-organized for both beginners and advanced learners. The trainers, experts in their domain, offer practical insights and real-world examples to enhance understanding.
Career Launch
Harini V
Data Science @ JPMorgan Chase
A well-organized platform for learning Data Science technologies. If you're looking to move into Data Scientist roles, this is the best place to acquire skills. The project work is the highlight, offering practical learning that's also beneficial after joining a company. Overall, it's a great platform for skill learning.
Career Launch
Shishir Shashank
Data Scientist @ Meta
A well-organized platform for learning Data Science technologies. If you're looking to move into Data Scientist roles, this is the best place to acquire skills. The project work is the highlight, offering practical learning that's also beneficial after joining a company. Overall, it's a great platform for skill learning.
Career Gap
Prem Raj
Generative AI Developer at Infosys
One of the best data science courses available online. The expert instructors were incredibly helpful, guiding me to successfully transition from software development to the AI field. The hands-on projects were invaluable, providing practical experience that facilitated my move into a Data Scientist role.
Career Upgrade
Samant Sagar
Data Scientist | Machine Learning Engineer
I liked Logicmojo's data science course for its amazing lectures and the always-helpful from expert team anytime, which really helped me land a job as a Data Scientist in Invent Health Inc. It's a best data science course currently available online with best quality.
Career Launch
Kishan Kumar Singh
Data Science & AI Enthusiasts
The Data Science course curriculum is of the best quality, complemented by outstanding project discussed in class. It helps me include these data science project in my resume. The materials are helpful even after the completion of the course.
Career Upgrade
Arpit Singh
Senior Software Engineer at MakeMyTrip
Thank you, Logicmojo Team. Your classes are amazing. All topics Like Statistics, ML, Deep Learning are covered sequentially, and the techniques and patterns of problem-solving and project work are the best parts of the course.
Career Upgrade
Diwakar Choudhary
Senior Engineering Manager at Visa
Excellent Course for Data Science, Very Straight to the Point ,In-Depth Coverage of Every Point in Live Classes. Specially Focus on Practical Implementation with Data Science Projects.
Career Transition
Sumit Upadhye
Data Engineer @ Zoho | NLP and Speech Processing
One of the Best Resources for Data Science Live Classes. Indepth concepts are covered for Data Science & AI with all topics expalined with more than 10 projects at the end. Logicmojo Course Helped me Multiple Times During Interview Preparation For Companies like Walmart, Oracle, and Microsoft.
career-launch
Afnaan Rafique
Software Engineer 2 at paypal
I have a very great experience with Logicmojo. Learning by doing is a great way to learn something which Logicmojo team encouraged me to do so. The course has a big contribution to my success.
Career Transition
Priya Singh
Software Engineer at Cisco
The Course Curriculum is of Best Quality Along with detail Project work on Data science Lab Work. Materials are Helpful even After Completion of the Course.
Career Gap
Aravind R
Sr. Business Analyst at Virtusa
Excellent Course for Interview Preparation, Very Straight to the Point ,In-Depth Coverage of Every Point in Live Classes. Speecially Focus on Practical Implementation of machine learning & Data Science in the classes.
career-gap
Piyush Mittal
Senior Software Engineer at Microsoft
The Course Curriculum is of the Best Quality along with the Best Learning Experience from my Tutor. Best Course to Prepare For top product companies Interview for Data Science role. I Cracked Paytm, Adobe, Intuit, and Microsoft. Finally joined Microsoft at 1.3 Cr LPA Hyderabad MS IDC
career-launch
Aravindo Swain
Software Engineer at Microsoft
I Would say the Best Part is the Explanation by the Trainer, Concise and Clear. Great Quality of Online Materials and Classes, It Covers all topics of ML & AI asked During Interviews
Career Transition
Kailash Patel
Data Engineer at Fractal Analytics
Logicmojo shines with its approachable instructors and structured curriculum, making it a great choice for mastering Data Science, especially for those struggling with Machine Learning. I was one of the regular student in the class and solve all assignments & projects so team help to build my portfolio
Career Transition
Sanchit Taliyan
Software Development Engineer 3 at ANAROCK Technology
The overall experience is good. The course is detailed and well-structured, with proper guidance. Mentor sessions are also beneficial as they inform you about what the industry is looking for in a candidate, how you can learn those skills, and what should be the best way or approach to learn Data Science tools.
Real Stories, Real Success
Reviews of the candidates enrolled in Logicmojo Data Science Course.
Kishan Kumar
Honeywell
Choosing Logicmojo was my best decision ever. The instructors were encouraging and glad to answer questions.
Read More
Sampada
HCL
Transitioning from a non-tech role, Logicmojo's data science certification equipped me with the skills needed in data scientist role
Read More
Jayanth Reddy
Virtusa
I had a great learning experience at Logicmojo.The faculties here are top notch. Right from enrollment to getting a good job, team helped me.
Read More
Arvind R
Virtusa
I moved from teaching to data science with Logicmojo's help. Their course was easy to follow.
Read More
Kailash Patel
Fractal Analytics
I moved from teaching to data science role with the help of Logicmojo data science course.
Read More
3. Data Science Course Syllabus
Data Science Curriculum
Module 1 - Python: From Basics to Advanced
Complete Python syllabus from basic to advanced
Topic
Duration:
13 Hours
- Working With Numbers, Booleans and Strings
- String types and formatting, String operations
- List, Tuples, Dictionaries Python Lists, Tuples, Dictionaries
- Basic Operations Indexing, Slicing, and Matrices
- Built-in Functions & Methods
- Exercises on List,Tuples And Dictionary
- Functions & Modules
- Exceptional Handling and Regular Expression
- Numpy, Pandas, Matplotlib, Seaborn, Scipy
- Introduction to Numpy
- Array Creation, Printing Arrays. Basic Operation
- Indexing, Slicing and Iterating, Shape Manipulation
- Vector stacking, Broadcasting with Numpy
- Numpy for Statistical Operation
- Introduction to Pandas
- Pandas Data Frames, Indexing Data Frames
- Basic Operations With Data frame
- Sub setting and filtering a data frame
- How to use & configure Matplotlib
- plot(), Controlling Line Properties
- Subplot with Functional Method, Multiple Plot
- Working with Multiple Figures
- Histograms
- Intro to Seaborn, Visualizing statistical relationships
- Plotting with categorical data
- Visualizing linear relationships
- Seaborn Exercise
- Resume Screening project with Python Library
- Climate Change impacts on the Global Food Supply
Chapter 1: Python deep-dive
Chapter 2: Library of Python used for ML/AI
Chapter 3: Data Analysis Using Numpy
Chapter 4: Data Analysis Using Pandas
Chapter 5: Data Visualization using Matplotlib
Chapter 6: Data Visualization using Seaborn
Project:
Module 2 - Applied Statistics in Machine Learning
Detailed Applied Statistics in Machine Learning
Topic
Duration:
13 Hours
- Working With Numbers, Booleans and Strings
- String types and formatting, String operations
- List, Tuples, Dictionaries Python Lists, Tuples, Dictionaries
- Basic Operations Indexing, Slicing, and Matrices
- Built-in Functions & Methods
- Exercises on List,Tuples And Dictionary
- Functions & Modules
- Exceptional Handling and Regular Expression
- Numpy, Pandas, Matplotlib, Seaborn, Scipy
- Introduction to Numpy
- Array Creation, Printing Arrays. Basic Operation
- Indexing, Slicing and Iterating, Shape Manipulation
- Vector stacking, Broadcasting with Numpy
- Numpy for Statistical Operation
- Introduction to Pandas
- Pandas Data Frames, Indexing Data Frames
- Basic Operations With Data frame
- Sub setting and filtering a data frame
- How to use & configure Matplotlib
- plot(), Controlling Line Properties
- Subplot with Functional Method, Multiple Plot
- Working with Multiple Figures
- Histograms
- Intro to Seaborn, Visualizing statistical relationships
- Plotting with categorical data
- Visualizing linear relationships
- Seaborn Exercise
- Resume Screening project with Python Library
- Climate Change impacts on the Global Food Supply
Chapter 1: Python deep-dive
Chapter 2: Library of Python used for ML/AI
Chapter 3: Data Analysis Using Numpy
Chapter 4: Data Analysis Using Pandas
Chapter 5: Data Visualization using Matplotlib
Chapter 6: Data Visualization using Seaborn
Project:
Module 3 - SQL
Learn SQL with query structures
Topic
Duration:
13 Hours
- Working With Numbers, Booleans and Strings
- String types and formatting, String operations
- List, Tuples, Dictionaries Python Lists, Tuples, Dictionaries
- Basic Operations Indexing, Slicing, and Matrices
- Built-in Functions & Methods
- Exercises on List,Tuples And Dictionary
- Functions & Modules
- Exceptional Handling and Regular Expression
- Numpy, Pandas, Matplotlib, Seaborn, Scipy
- Introduction to Numpy
- Array Creation, Printing Arrays. Basic Operation
- Indexing, Slicing and Iterating, Shape Manipulation
- Vector stacking, Broadcasting with Numpy
- Numpy for Statistical Operation
- Introduction to Pandas
- Pandas Data Frames, Indexing Data Frames
- Basic Operations With Data frame
- Sub setting and filtering a data frame
- How to use & configure Matplotlib
- plot(), Controlling Line Properties
- Subplot with Functional Method, Multiple Plot
- Working with Multiple Figures
- Histograms
- Intro to Seaborn, Visualizing statistical relationships
- Plotting with categorical data
- Visualizing linear relationships
- Seaborn Exercise
- Resume Screening project with Python Library
- Climate Change impacts on the Global Food Supply
Chapter 1: Python deep-dive
Chapter 2: Library of Python used for ML/AI
Chapter 3: Data Analysis Using Numpy
Chapter 4: Data Analysis Using Pandas
Chapter 5: Data Visualization using Matplotlib
Chapter 6: Data Visualization using Seaborn
Project:
Module 4 - Machine Learning
Practical hands-on in machine learning
Topic
Duration:
13 Hours
- Working With Numbers, Booleans and Strings
- String types and formatting, String operations
- List, Tuples, Dictionaries Python Lists, Tuples, Dictionaries
- Basic Operations Indexing, Slicing, and Matrices
- Built-in Functions & Methods
- Exercises on List,Tuples And Dictionary
- Functions & Modules
- Exceptional Handling and Regular Expression
- Numpy, Pandas, Matplotlib, Seaborn, Scipy
- Introduction to Numpy
- Array Creation, Printing Arrays. Basic Operation
- Indexing, Slicing and Iterating, Shape Manipulation
- Vector stacking, Broadcasting with Numpy
- Numpy for Statistical Operation
- Introduction to Pandas
- Pandas Data Frames, Indexing Data Frames
- Basic Operations With Data frame
- Sub setting and filtering a data frame
- How to use & configure Matplotlib
- plot(), Controlling Line Properties
- Subplot with Functional Method, Multiple Plot
- Working with Multiple Figures
- Histograms
- Intro to Seaborn, Visualizing statistical relationships
- Plotting with categorical data
- Visualizing linear relationships
- Seaborn Exercise
- Resume Screening project with Python Library
- Climate Change impacts on the Global Food Supply
Chapter 1: Python deep-dive
Chapter 2: Library of Python used for ML/AI
Chapter 3: Data Analysis Using Numpy
Chapter 4: Data Analysis Using Pandas
Chapter 5: Data Visualization using Matplotlib
Chapter 6: Data Visualization using Seaborn
Project:
Module 5 - Advanced Machine Learning
Advanced core concepts of machine learning
Topic
Duration:
13 Hours
- Working With Numbers, Booleans and Strings
- String types and formatting, String operations
- List, Tuples, Dictionaries Python Lists, Tuples, Dictionaries
- Basic Operations Indexing, Slicing, and Matrices
- Built-in Functions & Methods
- Exercises on List,Tuples And Dictionary
- Functions & Modules
- Exceptional Handling and Regular Expression
- Numpy, Pandas, Matplotlib, Seaborn, Scipy
- Introduction to Numpy
- Array Creation, Printing Arrays. Basic Operation
- Indexing, Slicing and Iterating, Shape Manipulation
- Vector stacking, Broadcasting with Numpy
- Numpy for Statistical Operation
- Introduction to Pandas
- Pandas Data Frames, Indexing Data Frames
- Basic Operations With Data frame
- Sub setting and filtering a data frame
- How to use & configure Matplotlib
- plot(), Controlling Line Properties
- Subplot with Functional Method, Multiple Plot
- Working with Multiple Figures
- Histograms
- Intro to Seaborn, Visualizing statistical relationships
- Plotting with categorical data
- Visualizing linear relationships
- Seaborn Exercise
- Resume Screening project with Python Library
- Climate Change impacts on the Global Food Supply
Chapter 1: Python deep-dive
Chapter 2: Library of Python used for ML/AI
Chapter 3: Data Analysis Using Numpy
Chapter 4: Data Analysis Using Pandas
Chapter 5: Data Visualization using Matplotlib
Chapter 6: Data Visualization using Seaborn
Project:
Module 6 - Deep Learning
Deep Learning: Basic to Advanced Session
Topic
Duration:
13 Hours
- Working With Numbers, Booleans and Strings
- String types and formatting, String operations
- List, Tuples, Dictionaries Python Lists, Tuples, Dictionaries
- Basic Operations Indexing, Slicing, and Matrices
- Built-in Functions & Methods
- Exercises on List,Tuples And Dictionary
- Functions & Modules
- Exceptional Handling and Regular Expression
- Numpy, Pandas, Matplotlib, Seaborn, Scipy
- Introduction to Numpy
- Array Creation, Printing Arrays. Basic Operation
- Indexing, Slicing and Iterating, Shape Manipulation
- Vector stacking, Broadcasting with Numpy
- Numpy for Statistical Operation
- Introduction to Pandas
- Pandas Data Frames, Indexing Data Frames
- Basic Operations With Data frame
- Sub setting and filtering a data frame
- How to use & configure Matplotlib
- plot(), Controlling Line Properties
- Subplot with Functional Method, Multiple Plot
- Working with Multiple Figures
- Histograms
- Intro to Seaborn, Visualizing statistical relationships
- Plotting with categorical data
- Visualizing linear relationships
- Seaborn Exercise
- Resume Screening project with Python Library
- Climate Change impacts on the Global Food Supply
Chapter 1: Python deep-dive
Chapter 2: Library of Python used for ML/AI
Chapter 3: Data Analysis Using Numpy
Chapter 4: Data Analysis Using Pandas
Chapter 5: Data Visualization using Matplotlib
Chapter 6: Data Visualization using Seaborn
Project:
Module 7 - Natural Language Processing
Natural Language Processing: Practical Workshop
Topic
Duration:
13 Hours
- Working With Numbers, Booleans and Strings
- String types and formatting, String operations
- List, Tuples, Dictionaries Python Lists, Tuples, Dictionaries
- Basic Operations Indexing, Slicing, and Matrices
- Built-in Functions & Methods
- Exercises on List,Tuples And Dictionary
- Functions & Modules
- Exceptional Handling and Regular Expression
- Numpy, Pandas, Matplotlib, Seaborn, Scipy
- Introduction to Numpy
- Array Creation, Printing Arrays. Basic Operation
- Indexing, Slicing and Iterating, Shape Manipulation
- Vector stacking, Broadcasting with Numpy
- Numpy for Statistical Operation
- Introduction to Pandas
- Pandas Data Frames, Indexing Data Frames
- Basic Operations With Data frame
- Sub setting and filtering a data frame
- How to use & configure Matplotlib
- plot(), Controlling Line Properties
- Subplot with Functional Method, Multiple Plot
- Working with Multiple Figures
- Histograms
- Intro to Seaborn, Visualizing statistical relationships
- Plotting with categorical data
- Visualizing linear relationships
- Seaborn Exercise
- Resume Screening project with Python Library
- Climate Change impacts on the Global Food Supply
Chapter 1: Python deep-dive
Chapter 2: Library of Python used for ML/AI
Chapter 3: Data Analysis Using Numpy
Chapter 4: Data Analysis Using Pandas
Chapter 5: Data Visualization using Matplotlib
Chapter 6: Data Visualization using Seaborn
Project:
Module 8 - Tableau
Tableau Complete Syllabus and Detailed Content
Topic
Duration:
13 Hours
- Working With Numbers, Booleans and Strings
- String types and formatting, String operations
- List, Tuples, Dictionaries Python Lists, Tuples, Dictionaries
- Basic Operations Indexing, Slicing, and Matrices
- Built-in Functions & Methods
- Exercises on List,Tuples And Dictionary
- Functions & Modules
- Exceptional Handling and Regular Expression
- Numpy, Pandas, Matplotlib, Seaborn, Scipy
- Introduction to Numpy
- Array Creation, Printing Arrays. Basic Operation
- Indexing, Slicing and Iterating, Shape Manipulation
- Vector stacking, Broadcasting with Numpy
- Numpy for Statistical Operation
- Introduction to Pandas
- Pandas Data Frames, Indexing Data Frames
- Basic Operations With Data frame
- Sub setting and filtering a data frame
- How to use & configure Matplotlib
- plot(), Controlling Line Properties
- Subplot with Functional Method, Multiple Plot
- Working with Multiple Figures
- Histograms
- Intro to Seaborn, Visualizing statistical relationships
- Plotting with categorical data
- Visualizing linear relationships
- Seaborn Exercise
- Resume Screening project with Python Library
- Climate Change impacts on the Global Food Supply
Chapter 1: Python deep-dive
Chapter 2: Library of Python used for ML/AI
Chapter 3: Data Analysis Using Numpy
Chapter 4: Data Analysis Using Pandas
Chapter 5: Data Visualization using Matplotlib
Chapter 6: Data Visualization using Seaborn
Project:
Module 9 - PowerBI
PowerBI Complete Syllabus and Detailed Content
Topic
Duration:
13 Hours
- Working With Numbers, Booleans and Strings
- String types and formatting, String operations
- List, Tuples, Dictionaries Python Lists, Tuples, Dictionaries
- Basic Operations Indexing, Slicing, and Matrices
- Built-in Functions & Methods
- Exercises on List,Tuples And Dictionary
- Functions & Modules
- Exceptional Handling and Regular Expression
- Numpy, Pandas, Matplotlib, Seaborn, Scipy
- Introduction to Numpy
- Array Creation, Printing Arrays. Basic Operation
- Indexing, Slicing and Iterating, Shape Manipulation
- Vector stacking, Broadcasting with Numpy
- Numpy for Statistical Operation
- Introduction to Pandas
- Pandas Data Frames, Indexing Data Frames
- Basic Operations With Data frame
- Sub setting and filtering a data frame
- How to use & configure Matplotlib
- plot(), Controlling Line Properties
- Subplot with Functional Method, Multiple Plot
- Working with Multiple Figures
- Histograms
- Intro to Seaborn, Visualizing statistical relationships
- Plotting with categorical data
- Visualizing linear relationships
- Seaborn Exercise
- Resume Screening project with Python Library
- Climate Change impacts on the Global Food Supply
Chapter 1: Python deep-dive
Chapter 2: Library of Python used for ML/AI
Chapter 3: Data Analysis Using Numpy
Chapter 4: Data Analysis Using Pandas
Chapter 5: Data Visualization using Matplotlib
Chapter 6: Data Visualization using Seaborn
Project:
Module 10 - Computer Vision
Detailed Practical Session on Computer Vision
Topic
Duration:
13 Hours
- Working With Numbers, Booleans and Strings
- String types and formatting, String operations
- List, Tuples, Dictionaries Python Lists, Tuples, Dictionaries
- Basic Operations Indexing, Slicing, and Matrices
- Built-in Functions & Methods
- Exercises on List,Tuples And Dictionary
- Functions & Modules
- Exceptional Handling and Regular Expression
- Numpy, Pandas, Matplotlib, Seaborn, Scipy
- Introduction to Numpy
- Array Creation, Printing Arrays. Basic Operation
- Indexing, Slicing and Iterating, Shape Manipulation
- Vector stacking, Broadcasting with Numpy
- Numpy for Statistical Operation
- Introduction to Pandas
- Pandas Data Frames, Indexing Data Frames
- Basic Operations With Data frame
- Sub setting and filtering a data frame
- How to use & configure Matplotlib
- plot(), Controlling Line Properties
- Subplot with Functional Method, Multiple Plot
- Working with Multiple Figures
- Histograms
- Intro to Seaborn, Visualizing statistical relationships
- Plotting with categorical data
- Visualizing linear relationships
- Seaborn Exercise
- Resume Screening project with Python Library
- Climate Change impacts on the Global Food Supply
Chapter 1: Python deep-dive
Chapter 2: Library of Python used for ML/AI
Chapter 3: Data Analysis Using Numpy
Chapter 4: Data Analysis Using Pandas
Chapter 5: Data Visualization using Matplotlib
Chapter 6: Data Visualization using Seaborn
Project:
Module 11 - Neural Networks
In-depth Neural Network Syllabus
Topic
Duration:
13 Hours
- Working With Numbers, Booleans and Strings
- String types and formatting, String operations
- List, Tuples, Dictionaries Python Lists, Tuples, Dictionaries
- Basic Operations Indexing, Slicing, and Matrices
- Built-in Functions & Methods
- Exercises on List,Tuples And Dictionary
- Functions & Modules
- Exceptional Handling and Regular Expression
- Numpy, Pandas, Matplotlib, Seaborn, Scipy
- Introduction to Numpy
- Array Creation, Printing Arrays. Basic Operation
- Indexing, Slicing and Iterating, Shape Manipulation
- Vector stacking, Broadcasting with Numpy
- Numpy for Statistical Operation
- Introduction to Pandas
- Pandas Data Frames, Indexing Data Frames
- Basic Operations With Data frame
- Sub setting and filtering a data frame
- How to use & configure Matplotlib
- plot(), Controlling Line Properties
- Subplot with Functional Method, Multiple Plot
- Working with Multiple Figures
- Histograms
- Intro to Seaborn, Visualizing statistical relationships
- Plotting with categorical data
- Visualizing linear relationships
- Seaborn Exercise
- Resume Screening project with Python Library
- Climate Change impacts on the Global Food Supply
Chapter 1: Python deep-dive
Chapter 2: Library of Python used for ML/AI
Chapter 3: Data Analysis Using Numpy
Chapter 4: Data Analysis Using Pandas
Chapter 5: Data Visualization using Matplotlib
Chapter 6: Data Visualization using Seaborn
Project:
Module 12 - Generative AI
Comprehensive Generative AI Practical Session
Topic
Duration:
13 Hours
- Working With Numbers, Booleans and Strings
- String types and formatting, String operations
- List, Tuples, Dictionaries Python Lists, Tuples, Dictionaries
- Basic Operations Indexing, Slicing, and Matrices
- Built-in Functions & Methods
- Exercises on List,Tuples And Dictionary
- Functions & Modules
- Exceptional Handling and Regular Expression
- Numpy, Pandas, Matplotlib, Seaborn, Scipy
- Introduction to Numpy
- Array Creation, Printing Arrays. Basic Operation
- Indexing, Slicing and Iterating, Shape Manipulation
- Vector stacking, Broadcasting with Numpy
- Numpy for Statistical Operation
- Introduction to Pandas
- Pandas Data Frames, Indexing Data Frames
- Basic Operations With Data frame
- Sub setting and filtering a data frame
- How to use & configure Matplotlib
- plot(), Controlling Line Properties
- Subplot with Functional Method, Multiple Plot
- Working with Multiple Figures
- Histograms
- Intro to Seaborn, Visualizing statistical relationships
- Plotting with categorical data
- Visualizing linear relationships
- Seaborn Exercise
- Resume Screening project with Python Library
- Climate Change impacts on the Global Food Supply
Chapter 1: Python deep-dive
Chapter 2: Library of Python used for ML/AI
Chapter 3: Data Analysis Using Numpy
Chapter 4: Data Analysis Using Pandas
Chapter 5: Data Visualization using Matplotlib
Chapter 6: Data Visualization using Seaborn
Project:
Topic
Duration:
13 Hours
- Working With Numbers, Booleans and Strings
- String types and formatting, String operations
- List, Tuples, Dictionaries Python Lists, Tuples, Dictionaries
- Basic Operations Indexing, Slicing, and Matrices
- Built-in Functions & Methods
- Exercises on List,Tuples And Dictionary
- Functions & Modules
- Exceptional Handling and Regular Expression
- Numpy, Pandas, Matplotlib, Seaborn, Scipy
- Introduction to Numpy
- Array Creation, Printing Arrays. Basic Operation
- Indexing, Slicing and Iterating, Shape Manipulation
- Vector stacking, Broadcasting with Numpy
- Numpy for Statistical Operation
- Introduction to Pandas
- Pandas Data Frames, Indexing Data Frames
- Basic Operations With Data frame
- Sub setting and filtering a data frame
- How to use & configure Matplotlib
- plot(), Controlling Line Properties
- Subplot with Functional Method, Multiple Plot
- Working with Multiple Figures
- Histograms
- Intro to Seaborn, Visualizing statistical relationships
- Plotting with categorical data
- Visualizing linear relationships
- Seaborn Exercise
- Resume Screening project with Python Library
- Climate Change impacts on the Global Food Supply
Chapter 1: Python deep-dive
Chapter 2: Library of Python used for ML/AI
Chapter 3: Data Analysis Using Numpy
Chapter 4: Data Analysis Using Pandas
Chapter 5: Data Visualization using Matplotlib
Chapter 6: Data Visualization using Seaborn
Project:
4. Which Tools You will learn
During the preparation process in the course, you'll become proficient with a variety of tools that are essential for data science, big data, powerBI and tableau. Here are some of the key tools you will learn
Python
NumPy
Scikit-learn
Pytorch
Matplotlib
TensorFlow
Keras
OpenCV
NLTK
Gbard
ChatGPT
DALL-E-2
5. Course Completion Certificate
Elevate your career with our esteemed certification. Unlock new opportunities and demonstrate your mastery in data science.
Industry Recognised Certification
Upon successfully completing data science projects, you’ll earn an advanced certification in data science work experience, jointly offered by Logicmojo and its partner company.
Complete the project under the 1:1 guidance of a senior data scientist
Access to lectures from top faculties of IIT Guwahati
Real-world data science project experience will be included in your resume
Logicmojo team will modify your resume and include these data science projects during the resume preparation sessions.
Click to zoom
6. Industry Projects
Projects will be a part of your Certification in Data Science Course to consolidate your learning. It will ensure that you have real-world experience in Data Science Field.
Practice 20+
Essential Tools
Designed by
Industry Experts
Industry Projects
Work Experience
AI Chatbot
Develop an AI chatbot to provide real-time user assistance and personalized responses.
Use NLP and machine learning to provide real-time data and Sentiment Analysis.
ML
NLP
GenAI
Statistics
Face Detection
Face detection system to identify and analyze human faces in images and videos.
This Project uses computer vision and deep learning for analysis.
GenAI
Deep Learning
Computer Vision
TensorFlow
Insurance Prediction
Develop a predictive model health insurance to estimate costs based on individual health.
Use Machine Learning, Regression Analysis and Data Preprocessing to Predict Health Insurance.
ML Models
Regression Analysis
Neural Networks
Decision Trees
Revenue Prediction
Develop a machine learning model to predict restaurant revenue based on historical data.
Use Time Series Analysis, Random Forests and ML Models to Predict Restaurant Revenue.
Random Forests
Statistics
Data Preprocessing
Time Series Analysis
Twitter Reviews
Twitter customer reviews clustering, topic modeling can be used to identify the main themes or topics
Use NLP, Text Preprocessing, Clustering Algorithms to to cluster the twitter reviews
NLP
ML Models
Text Embedding Models
Statistics
Banknote Classification
Authentic vs. Fake Banknote Classification.
Using supervised learning algorithms such as logistic regression, decision trees, random forests to identify.
Regression
Decision Trees
Deep Learning
ML
Credit Card Approval
Help banks find potential customers to give out credit cards to by building using Statistics, ML models.
Use classification models like Logistic Regression and NLP.
ML Models
Python
Statistics
Regression
Housing Price Prediction
Uncover housing market trends effortlessly with powerful Python libraries and statistics.
Explore location, size, income, amenities, and more to uncover trends by using predictive analysis.
Python
Statistics
EDA
ML
E-commerce Project
Use clustering techniques to group customers based on their purchasing behavior.
Uses Clustering Algorithms, Dimensionality Reduction algorithms to clouster customers.
ML Models
Python
Statistics
ML
Fraud Detection
Use anomaly detection algorithms and machine learning models to identify fraudulent.
Uses ML Algorithms, Data Preprocessin to detect the fraud in transaction.
Python
Statistics
EDA
ML
Health Insurance Prediction
Develop a predictive model health insurance to estimate costs based on individual health.
Use Machine Learning, Regression Analysis and Data Preprocessing to Predict Health Insurance.
ML Models
Regression Analysis
Neural Networks
Decision Trees
Restaurant Revenue Prediction
Develop a machine learning model to predict restaurant revenue based on historical data.
Use Time Series Analysis, Random Forests and ML Models to Predict Restaurant Revenue.
Random Forests
Statistics
Data Preprocessing
Time Series Analysis
Credit Card Approval
Help banks find potential customers to give out credit cards to by building using Statistics, ML models.
Use classification models like Logistic Regression and NLP.
ML Models
Python
Statistics
Regression
Twitter Customer Reviews
Twitter customer reviews clustering, topic modeling can be used to identify the main themes or topics
Use NLP, Text Preprocessing, Clustering Algorithms to to cluster the twitter reviews
NLP
ML Models
Text Embedding Models
Statistics
Housing Price Prediction
Uncover housing market trends effortlessly with powerful Python libraries and statistics.
Explore location, size, income, amenities, and more to uncover trends by using predictive analysis.
Python
Statistics
EDA
ML
Credit Card Approval
Help banks find potential customers to give out credit cards to by building using Statistics, ML models.
Use classification models like Logistic Regression and NLP.
ML Models
Python
Statistics
Regression
6. Career Services
Career Services in a Nutshell
Placement Support
Complete support from our dedicated placement team until you get a job
Resume Building
Resume sharing in our network of 700+ hiring partners
Logicmojo Job Application
End to end application tracking of multiple interview rounds
Mock Interviews
Professional mock interviews assistance
Dedicated Career Coach
Expert data science career coaches to guide you throughout the program
Assured Selection
Get placed in a Data Scientist role in a Product Organization
Land your dream job at one of the leading tech companies
7. Meet Our Mentors
Our Data Science Course Industry Mentors Demo Class
Demo Class
Head instructor
For Current Batch
Head Instructor
Monesh has over 6+ years of experience as a Data Scientist and Senior Machine Learning Engineer at renowned organizations, contributing to various projects in Generative AI and Data Science. He also has more than 4+ years of teaching experience as a corporate trainer.
Ranjan Kumar
Data Science Mentor @ Amazon, Microsoft
Ranjan is currently working as a senior Developer in Amazon. He has a vast experience of teaching Data Science.
Mohamed Shirhaan
Senior Data Science Mentor
Shirhaan working as R & D Group of WalmartLabs as senior Developer.
Sankalp Jain
Senior Mentor Data Scientist
Sankalp is From IIT Kgp worked as a senior developer in Odin AI as a Data Scientist. Experienced in the field of Data Scientist & AI.
Ravi Singh
Senior Mentor Data Scientist
Ravi has more than 14 years of experience and has worked on multiple projects in data science and machine learning, as well as in business analytics.
8. Why Logicmojo ?
Live online classes led by instructorsVery high value for moneyIndustry-aligned projects with project presentation guidanceWith a dedicated team of expertsExtensive services, including resume-building and behavioral skills workshopsA dedicated support teamFor higher salaries9. How to Enroll ?
Step 1
Submit Application
Book calender call with our senior data scientist
Step 2
Application Review
Data scientist will review your profile & answer all your queries
Step 3
Admission
Candidate can apply for Online Live classes for 8 months
Apply For Live Course
10. Course Fee ?
Total Fee
₹ 54,000
No Cost EMI
As low as ₹ 4,500/month
Apply nowWe have partnered with the following financing companies to provide competitive finance options at 0% interest rate with no hidden costs...
What Is the Eligibility Criteria for the Data Science Course?
A bechelor's degree with an average of 50% or higher marks
Basic understanding of programming concepts and mathematics
2+ years of work experience preferred
Who can apply for the Data Science course?
- Software engineers
- IT Professionals
- Data Professionals
- Business Analysis
- Product Managers
11. Frequently Asked Questions
Everything you need to know about the courses, tuition fees, and more.
What topics are covered in a Data Science Training Program? +
Which language is best for learning Data Science? +
Anjani Kumar
Praveen Kumar
Aman
Ashish
Asha Yadav
Himanshu Mittal
Arpan Banerjee
Shivank Agarwal
Gunjan Yadav
Vamsi Karuturi
Piyush Mittal
Salin Gupta
Rishav
Anjani Kumar
Praveen Kumar
Aman
Ashish
Asha Yadav
Himanshu Mittal
Arpan Banerjee
Shivank Agarwal
Gunjan Yadav
Vamsi Karuturi
Piyush Mittal
Salin Gupta
Rishav
11. Data Science Blogs
Contact US
Got more questions?
Talk to our team directly
Reach us and a learning consultant will get in touch with you shortly
Enquire now +91 80889-75867Contact Us
IND: +91 80889-75867
Email: info@logicmojo.com
Location: Marathahalli, Bangalore, India
Project Innovation Lab Address
Pune
Sky Loft Group Floor, Creaticity Mall, Opposite Golf Course, Airport Rd, Shastrinagar, Yerawada, Pune, Maharashtra 411006
Mumbai
Business Centre, 3rd Floor, vinkat House, Dinshaw Vacha Road, Churchgate, Mumbai, Maharashtra 400020
Delhi
Business Centre Park, Arunachal Building, 8th Floor, Barakhamba Road, New Delhi 110001
Kolkata
Ambuja Neotia EcoCentre 5th Floor, EM-4,EM-Block, Sector 6 Kolkata 700091
Hyderabad
NSL Centrum, Road No 1, Lane, Opp. Forum Mall Central Hub, KPHB 4th Phase, Hyderabad, Telangana 500072
Bangalore
#23, Above Veena Mobiles, 14th Main Road, Opp-White House, Sector 2, Hsr Layout, Bangalore 560102
Chennai
Cessanana Business Centre, Phone Booth, 3rd Floor, 39/12 Haddows Road, Nungambakkam, Chennai 600 006
© 2024 LOGICMOJO. ALL RIGHTS RESERVED
