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Data Science Course in Bangalore
Join the Data Science course in Bangalore at Apponix Academy and learn Python, SQL, statistics, machine learning, and deep learning from professionals who work in data and AI at Accenture, TCS, Intel, and Mindtree. Every module ends with a hands-on project, so you graduate with a portfolio, not just notes.
Study in person at Rajajinagar or Marathahalli, or join live online classes from anywhere. Our Data Science training in Bangalore is designed for freshers, working professionals, and career switchers, with no maths degree required.
2000+ Ratings
3000+ Happy Learners
60+Hours of Live Classes
6Capstone Projects
20+Tools & Libraries
100%Placement Support
1-YearRecording Access
Testimonials By Our Students For Data Science Course
Skills you will gain in our Data Science course
Python Programming from Scratch
Data Visualization with Python
Machine Learning with Python
Statistical Analysis with Python
Data Manipulation with Pandas
SQL Queries with Python
Supervised & Unsupervised Learning
Building Predictive Models
Time Series Analysis
Model Evaluation & Optimization
Key Features of Data Science Course in Bangalore
12 real-world case studies
6 hands-on projects, including a capstone
Trainers working in data and AI roles
Portfolio and GitHub profile guidance
Resume building and mock interviews
Communication and presentation practice
A placement advisor assigned to you
Guidance for Microsoft, Google, and IBM certifications
Weekly coding challenges
Classroom or live online, with a year of recordings
The Data Science Toolkit You Will Work With
Data scientists rarely rely on a single tool. Throughout the course you move between languages, libraries, and platforms the way a working team does, so the stack feels familiar by the time you interview.
Python
The core language for data work, taught from scratch with plenty of practice.
NumPy & Pandas
Handle tables, missing values, and large datasets quickly.
Matplotlib & Seaborn
Turn numbers into charts that explain patterns at a glance.
SQL & MySQL
Pull exactly the data you need from relational databases.
Scikit-learn
Build and compare classic machine learning models with a consistent workflow.
TensorFlow & Keras
Create deep learning models for image and text problems.
Jupyter Notebook & Google Colab
Experiment, document, and share your analysis in one place.
Power BI & Tableau
Build dashboards that decision-makers can explore themselves.
Streamlit & Flask
Wrap your models in a simple app or API that others can use.
Placement of Our Students in Top Companies
Data Science Course Reviews
Resham Rajpal
Data Science Expert
The training at Apponix made me more confident in Data analysis. I have completed Data Science training form here. Excellent classes that helped a lot.
Ajith Bahadhur
Data Science Analyst
My experience with Apponix was really great. The trainer provided a lot of practical training. He is an expert. Each topic was taught through proper practical classes.
Mahesh Mahi
Data Science Analyst
I took Data Science training from the institute. I am happy to make Apponix as my choice. Too good labs and sessions are handled here.
Lakshana Senthil
Data Engineer
Management is very good, flexible with timings. Teaching pattern is easy and understandable.
Bijal Patel
Data Analyst
Best institute for Data Science. Trainer has very good knowledge in Data Science.
Aarsh Mehta
Data Engineer
Sir is very helpful and takes every step to make the students understand topics.
Our Recent Placements
Classroom Training
Choose Classroom or Online Training
Class room Training
Upto ₹ 40,000
- In-person sessions with your trainer
- 60+ hours of hands-on coding
- Taught by practising data professionals
- Recordings available for 1 year
- Placement support and [5] interview arrangements
- Daily 2 hrs or weekend 10 hrs
- Regular assignments and reviews
Online Training
Upto ₹ 40,000
- Live sessions with screen sharing and Q&A
- 60+ hours of hands-on coding
- Taught by practising data professionals
- Recordings available for 1 year
- Placement support and [5] interview arrangements
- Daily 2 hrs or weekend 10 hrs
- Regular assignments and reviews
Flexible Payment Options
- Zero-interest EMI available across major banks
- Instalment-based payment plans
- Group and student discounts on request
- Early enrolment discounts for upcoming batches
Call +91 80505 80888 or to receive the latest fee structure, discounts, and enrolment offers for the Data Science Course in Bangalore.
Data Science Training Syllabus
Prerequisites
No prerequisites for learning data science but if you have some previous knowldge on computer science & statistics it helps you a lot in understanding subjects fast.
Secondly, you need to have an analytical mind – that is all you would need to enroll for Statistics For Data Science Courses.
Data Science Course Syllabus
Overview of the course
Class 1: Introduction to Python Programming Language
- Introduction and Installation of Python software Python packages: Pandas, & Numpy
- Concepts of Data frame Filtering
- Loc and iloc for filtering Usage of Boolean in Filtering Appending
Class 2: Data handling in Python
- Handling of Missing values If else statement
- Extra trick of using if else statement Removal of Duplicates
- Frequency Distribution
- Merging – Inner, Outer, Left and Right Binding and Appending
- Descriptive Statistics o Inbuilt Numeric functions of R
Class 3: More data handling using Python
- Pivot Table of Excel in Python Grouping function
- Learning of SQL queries using Python Grouping numeric data
Class 4: Additional functions of Python
- Text functions
- Data cleaning with efficient text functions Inbuilt String functions of Python Reshape functions of Python
Class 5: Statistics
- Everything you want to know about statistics….Well sort of!! Mean, Median, Mode
- Standard Deviation, Variance, Normal Distribution Hypothesis testing
- T-test, Anova, Normality test
Class 6: Linear Regression
Class 7 : Linear Regression Practice Case Study
- Predictive Analytics – Linear Regression Concepts of Linear Regression
- Simple and Multiple Linear Regression Automatic Dummy Variablescreation technique Model Validation parameters
- Model Assumption testing
- Splitting of data for Validation and testing
- Business Case Study with real data to model in Python
Participants will be asked to develop a Linear Regression model on a real life data, in presence of the instructor. Time given is 2.5 hours. Participants will be treated like an industry employee, but in terms of help certainly the instructor will not be as ruthless as the boss. After completion of the model (with the help of the instructor wherever it is required), the instructor will show how to present a model to a real life client.
Class 8: Logistic Regression
Class 9: Logistic Regression Practice Case Study
- Predictive Analytics – Logistic Regression Concepts of LogisticRegression
- Difference between Linear Regression and Logistic RegressionAutomatic Dummy Variables creation technique
- Model Validation parameters Model Assumption testing
- Splitting of data for Validation and testing
- Business Case Study with real data to model in Python
Participants will be asked to develop a Logistic Regression model on a real life data, in presence of the instructor. Time given is 2.5 hours. Participants will be treated like an industry employee, but in terms of help certainly the instructor will not be as ruthless as the boss. After completion of the model (with the help of the instructor wherever it is required), the instructor will show how to present a model to a real life client.
Class 10: Time Series Forecasting
- Time series forecasting: ARIMA
- Difference between forecasting and prediction Concepts of time series data
- Concepts of ARIMA
- Descriptive analytics for ARIMA Development of model
- Best model selection Forecasting with the best model Residual analysis
- Business Case Study with real data to model in R software
- Participants will be asked to develop a model in presence of the instructor.
Class 11: Cluster Analysis
- Unsupervised Machine Learning with Python Cluster Analysis: Concepts
- Cluster analysis with Python – K Means, Hierarchical etc.
Class 12: Decision Tree and Random Forest
- Concepts of Decision Tree Decision Tree with Python Concepts ofRandom Forest Random Forest with Python
Call Us On
+91 80505-80888
Our Top Instructors
Mr Shubhojit
Work Experience:
Shubhojit brings over 14 years of experience in Analytics, having worked with top CMMI Level 5 companies such as The Nielsen Company and TCS, where he currently serves as a Senior Analytics Consultant.
He has domain expertise across Retail, BFSI, Energy, and Utilities, and specializes in Predictive Analytics, Machine Learning, and Simulation using tools like SAS, R, Python, and GMP.
Proficient in advanced algorithms including ANN, RNN, CNN, XGBoost, Naïve Bayes, and SVM. He has built classification, regression, and churn models using Python and R.
Experienced in NLP for sentiment analysis, topic modeling, and text analytics. He has also applied recommendation systems such as Collaborative Filtering and Market Basket Analysis.
Shubhojit has utilized tools like Power BI, Spotfire, and REST APIs for building insightful dashboards and data integration, and used Google Analytics and HotJar for web and customer behavior analytics.
Trained 250+ students and conducted corporate sessions for Genpact, HSBC, ITC, and Wipro; also invited to IIT Kharagpur for workshops.
He had also been invited to IIT Kharagpur several times, to conduct analytics workshops for their B-Tech students. He has founded Analytics Educator – a premium institute for pertaining analytics training to professionals.
Founder of Analytics Educator and associated with institutions like Apponix, IVY Pro School, and EduPristine for professional analytics training.
Santosh
Education:
Graduate of Oxford College, where I developed a strong foundation in data science and analytics.
Professional Experience:
With over 10 years of experience in the industry, I am currently a Senior Data Science Specialist at Microsoft, specializing in advanced data analytics solutions for leading MNCs.
Technical Skills & Key Technologies:
- Predictive Modeling: Building models to forecast outcomes using historical data.
- Natural Language Processing (NLP): Analyzing and interpreting text data for insights.
- Machine Learning with Python: Skilled in applying multiple ML algorithms to solve real-world problems.
- Data Visualization: Creating impactful visuals using Python for better data storytelling.
- Robotic Process Automation (RPA): Streamlining tasks and boosting efficiency through automation.
Student Success & Career Support:
- Trained over 1000 students at Apponix Academy with an average rating of 4.9. I provide:
- Career Mentorship: Resume building, mock interviews, and personalized job readiness coaching.
- Placement Support: Assisting students in securing roles at top organizations through industry-focused guidance.
Subojith
Education:
Graduate from CMR University, where I built a strong foundation in data science principles and practices.Professional Experience:
With 15+ years of experience in the data science domain, I currently serve as a Senior Data Science Manager at Capgemini, bringing deep insights and knowledge from various MNCs.Technical Skills:
- Logistic Regression: Skilled in statistical modeling for binary outcomes and predictive analysis.
- SQL Queries with Python: Proficient in data manipulation and analysis using integrated SQL and Python workflows.
- Time Series Analysis: Experienced in identifying trends and patterns in temporal data.
- Cloud Computing (AWS, Azure, GCP): Leveraging cloud platforms for scalable data storage and analytics.
Student Success & Career Support:
- Trained over 1500 students at Apponix Academy with an average rating of 4.9, offering strong career guidance through:
- Resume Building: Crafting job-ready, data science-focused resumes.
- Interview Prep: Conducting mock sessions and sharing strategic tips.
- Placement Support: Assisting students in landing roles across top companies.
Keerthan
Education:
Graduated from Reva University, where I laid the foundation for my expertise in data science.
Professional Experience:
With 9+ years of experience in the industry, I currently serve as a Senior Data Science Specialist at Wipro, bringing valuable insights from my work with various MNCs.
Technical Skills:
- Machine Learning with Python: Proficient in applying 6 different algorithms to solve real-world problems.
- Robotic Automation: Expertise in automating processes to enhance efficiency.
- Multiple Linear Regression: Skilled in statistical modeling to understand relationships between variables.
- Data Visualization: Utilizing tools like Tableau and Matplotlib to present insights effectively.
- Big Data Technologies: Experience with frameworks like Hadoop and Spark for large-scale data processing.
Student Success & Career Support:
- I have trained over 900 students at Apponix Academy, achieving an average rating of 4.8. My commitment to student success includes:
- Resume Building: Helping students craft impactful resumes tailored for data science roles.
- Interview Preparation: Providing practical guidance and mock interviews to enhance confidence and skills.
- Job Placement Assistance: Supporting students in navigating the job market to secure fulfilling positions.
Arpitha
Education:
Alumnus of St. Joseph’s College, where I honed my analytical skills and passion for data science.
Professional Experience:
With 12+ years of experience, I currently serve as a Senior Data Science Specialist at Amazon, where I apply my expertise to solve complex data challenges in a fast-paced environment.Technical Skills & Expertise:
- Data Engineering & Wrangling: Skilled in ETL processes, cleaning, and preparing data for analysis.
- Experimental Design & A/B Testing: Designing experiments to support data-driven decisions.
- Model Deployment & Time Series Analysis: Deploying models and analyzing sequential data trends.
- Computer Vision & Web Scraping: Extracting insights from images, videos, and online data sources.
- Big Data & Visualization: Proficient with Hadoop, Spark, and creating impactful data visualizations.
Student Success & Career Support:
- Trained over 1200 students at Apponix Academy with an average rating of 4.8. I offer end-to-end support through:
- Resume Building: Creating impactful, job-ready resumes.
- Interview Prep & Placement: Mock interviews and personalized guidance to secure data science roles.
Data Science Course Overview
The Data Science Course in Bangalore at Apponix is designed to help students and professionals build strong practical skills in Machine Learning, Python Programming, and Artificial Intelligence. This industry-oriented training program combines theoretical concepts with real-time projects, enabling learners to gain hands-on experience with tools and technologies used by leading companies.
The course is suitable for fresh graduates, working professionals, career switchers, and non-programmers who want to enter the fast-growing field of Data Science. With expert-led sessions, live project training, and personalised mentorship, learners develop the confidence to solve real-world business problems using data-driven approaches.
Our curriculum covers Python Programming, SQL, Statistics, Machine Learning, Data Visualisation, AI fundamentals, and cloud-based technologies. As a trusted training institute in Bangalore, Apponix also provides resume-building support, mock interviews, career guidance, and globally recognised IABAC certification to improve placement opportunities with top IT and analytics companies.
Available in classroom, online, and hybrid learning modes, this Data Science Course in Bangalore focuses on practical implementation and job readiness. By the end of the training, learners will be equipped with the skills required for high-demand roles such as Data Scientist, Data Analyst, Machine Learning Engineer, and Business Intelligence Analyst.
Benefits of Enrolling in Our Data Science Course
Studying data science with Apponix Academy opens up a wealth of opportunities to fast-track your career in Bengaluru's thriving tech hub. Here are the key benefits that set our programme apart:
- Industry-Aligned Skills: Master Python, machine learning, SQL, and analytics through 18+ real-world case studies, including sales forecasting and customer insights, so you're job-ready for roles at companies like Google and Amazon.
- 90-Day Job Guarantee: Secure a role within 90 days with 8+ guaranteed interview calls, backed by our 95% placement success rate, connecting you to employers such as TCS, Wipro, and Accenture.
- Globally Recognised Certifications: Earn the IABAC Certified Data Scientist and JainX Advanced Certificate credentials, strengthening your resume's credibility worldwide.
- Free Add-Ons: Get complimentary AWS, Unix, and 12-month LinkedIn Learning access (worth ₹35,800), extending your skillset beyond the core syllabus.
- Flexible Learning: Choose classroom sessions in Marathahalli, online, or hybrid formats, tailored to your schedule and led by trainers with 6+ years of industry experience.
- Personalised Support: Benefit from one-on-one career counselling, resume building, and mock interviews to help you stand out in Bengaluru's competitive job market.
Data Science vs Machine Learning vs Artificial Intelligence: How Do They Differ?
These three terms are often used as if they mean the same thing, which makes choosing a course confusing. In short, data science is a broad field, machine learning is one of its main methods, and AI is the wider goal of building systems that act intelligently.
| Aspect | Data Science | Machine Learning | Artificial Intelligence |
|---|---|---|---|
| In one line | Extracting insights and predictions from data | Teaching computers to learn patterns from data | Building systems that perform tasks needing human-like intelligence |
| Scope | Broad: data cleaning, analysis, modelling, communication | Narrower: algorithms and model building | Widest: includes ML, vision, language, robotics |
| Key Skills | Python, SQL, statistics, visualisation, ML | Python, maths, algorithms, model tuning | Deep learning, NLP, computer vision, MLOps |
| Typical Roles | Data Scientist, Data Analyst | ML Engineer | AI Engineer, Research Engineer |
| Maths Level | Moderate to High | High | High |
| Fresher Salary in Bangalore | ₹4–9 LPA | ₹5–10 LPA | ₹5–11 LPA |
| Best Starting Point? | Yes, it covers the foundations of all three | After data science basics | After ML basics |
That is why our course starts with data science fundamentals and then moves into machine learning and deep learning. You build the base once and can specialise in either direction later.
Course Objectives
Data science is a fast-growing field, and proficiency in Python has become an essential skill across industries. This training gives you hands-on experience with the Python libraries used in real-world analytics work, along with practical skills you can apply from day one. Here's what the programme is built to deliver:
- Career Advancement: As demand for data professionals grows, a recognised certification can set you apart in the job market and add weight to your professional profile.
- Comprehensive Understanding: The curriculum covers the core concepts, tools, and techniques of the field, including NumPy, Pandas, Matplotlib, and Scikit-learn, so you can manipulate data, build predictive models, and visualise results with confidence.
- Hands-On Experience: You'll work on live projects using real datasets throughout the programme, building a portfolio you can show prospective employers.
- Personalised Instruction: Trainers provide individual guidance on data manipulation, visualisation, and machine learning, with feedback along the way to help you meet your learning goals.
- Flexibility: Sessions can be taken online at your own pace, with materials accessible anytime and trainer support available throughout.
Why is Python so widely used in this field?
Python's popularity comes down to its accessibility, its large developer community, and the breadth of libraries available for analysis and machine learning. It's a versatile language, equally suited to web development and scientific computing.
Combining strong data-handling capabilities with straightforward programming syntax is what makes Python such an effective tool for the work: manipulating large datasets, building predictive models, and visualising results, all supported by frameworks such as Scikit-learn, TensorFlow, and PyTorch.
