Data Science Course in Delhi
Data Science training in Delhi has become one of the most in-demand career programs today, offering opportunities in diverse industries such as IT, finance, healthcare, marketing and e-commerce. A Data Science course equips learners with essential tools for data analysis, predictive modelling and data-driven decision making – with Delhi serving as an important tech and business hub providing them access to industry training as well as career centric education opportunities.
- English
- English, Hindi
Upcoming Batch Weekdays!!!
Starting from Upcoming Weekend!
10:00 am – 01:00 pm Weekends
Fully Interactive Classroom Training
- 90 Hours Online Classroom Sessions
- 11 Module 04 Projects 5 MCQ Test
- 6 Months Complete Access
- Access on Mobile and laptop
- Certificate of completion
65,000 Students Enrolled
What we will learn
- Introduction to Data Science
- Statistics for Data Science
- Data Visualization and Business Intelligence
- Programming Essentials
- Machine Learning
- Big Data Technologies
- Data Analysis and Preprocessing
- Advanced Topics and Specializations
- Database Management
At our Data Science course in Delhi, you will gain the skills required to transform raw data into actionable insights. Starting with Python and R programming fundamentals, data analysis techniques, visualization methods, as well as unsupervised and supervised machine learning algorithms (e.g. deep learning), natural language processing (NLP), as well as database administration skills in SQL for database handling purposes and Tableau dashboard creation – hands-on projects ensure real world industry readiness!
Who Should Attend Data Science Course in Delhi
Our Delhi Data Science course is ideal for students, fresh graduates, IT professionals, business analysts and professionals from finance or marketing backgrounds looking to make the transition into data-driven roles. No prerequisites are necessary – basic mathematics knowledge and an analytical mindset will do.
Want to Discuss Your Roadmap to Be a Data Scientist in Delhi?
Our specialists help create a personalized roadmap tailored specifically to your goals – skill development and project guidance to career transition support are just a few aspects that we offer as part of this service. With numerous batch access sessions available from industry expert trainers as well as flexible learning, certifications, flexible learning formats, and clear success plans all guaranteed.
Advantages
Countless Batch Access
Industry Expret Trainers
Shareable Certificate
Learn from anywhere
Career Transition Guidance
Real-Time Projects
Industry Endorsed Curriculum
Interview Preparation Techniques
Class recordings
Course Mentor
Kushal Dwivedi
- 10 + Batches
- 4.8 Star Rating
- 859 Students Trained
- 450+ Successfully Placed
Hi, I’m Kushal Dwivedi, and I’m excited that you’re here.
Professionally, I am a Data Engineering mentor with strong industry exposure and hands-on experience in building scalable data solutions. I have successfully delivered 10+ batches and trained 859+ students, helping them understand data engineering concepts from fundamentals to advanced levels. With a 4.8-star rating and 450+ successful placements, I focus on practical learning, real-time tools, and industry use cases. In this course, you’ll learn how I combine real-world experience with structured, step-by-step teaching to help you build job-ready data engineering skills.
Data Science Course Content
- Probability
- Random Variables
- Probability Distribution
- Central Limit Theorem
- Sampling
- Confidence Intervals
- Hypothesis Testing
- Chi Square Test
- Anova Test
- Data Types
- Basic statistics using data examples
- Central tendencies
- Correlation analysis
- Data Summarization
- Data Dictionary
- Outliers /Missing Values
- Basic Linear Algebra – dot product, matrix multiplication and transformations
- Overview
- The Python Ecosystem
- Why Python over R/SAS
- What to expect after you learn Python
- Understanding and choosing between different Python versions
- Setting up Python on any machine (Windows/Linux/Mac)
- Using Anaconda, the Python distribution
- Exploring the different third-party IDEs (PyCharm, Spyder, Jupyter, Sublime)
- Setting up a suitable Workspace
- Running the first Python program
- Python Syntax
- Interactive Mode/ Script Mode Programming
- Identifiers and Keywords
- Single and Multi-line Comments
- Data Types in Python (Numbers, String, List, Tuple, Set, Dictionary)
- Implicit and Explicit Conversions
- Understanding Operators in Python
- Working with various Date and Time formats
- Working with Numeric data types – int, long, float, complex
- String Handling, Escape Characters, String Operations
- Working with Unicode Strings
- Local and Global Variables
- Flow Control and Decision Making in Python
- Understanding if else conditional statements
- Nested Conditions
- Working in Iterations
- Understanding the for and while Loop
- Nested Loops
- Loop Control Statements– break, continue, pass
- Understanding Dictionary- The key value pairs
- List Comprehensions and Dictionary Comprehensions
- Functions, Arguments, Return Statements
- Packages, Libraries and Modules
- Error Handling in Python
- Reading data from files (TXT, CSV, Excel, JSON, KML etc.)
- Writing data to desired file format
- Creating Connections to Databases
- Working in Iterations
- Importing/Exporting data from/to NoSQL databases (MongoDB)
- Importing/Exporting data from/to RDBMS (PostgreSQL)
- Getting data from Websites
- Manipulating Configuration files
- Introduction to Data Wrangling Techniques
- Why is transformation so important
- Understanding Database architecture – (RDBMS, NoSQL Databases)
- Understanding the strength/limitations of each complex data containers
- Understanding Sorting, Filtering, Redundancy, Cardinality, Sampling, Aggregations
- Converting from one Data Type to another
- Introduction to Numpy and its superior capabilities
- Understanding differences between Lists and Arrays
- Understanding Vectors and Matrices, Dot Products and Matrix Products
- Universal Array Functions
- Understanding Pandas and its architecture
- Getting to know Series and DataFrames, Columns and Indexes
- Getting Summary Statistics of the Data
- Data Alignment, Ranking & Sorting
- Combining/Splitting DataFrames, Reshaping, Grouping
- Identifying Outliers and performing Binning tasks
- Cross Tabulation, Permutations, the apply() function
- Introduction to Data Visualization
- Line Chart, Scatterplots, Box Plots, Violin Plots
- What is machine learning
- Different stages of ML project
- Supervised vs Unsupervised ML
- Algorithms in Supervised and Unsupervised learning
- Introduction to Sklearn
- Data preprocessing
- Scaling techniques
- Training /testing / validation datasets
- Feature Engineering
- How to deal with Categorical Variables – Dummy variables
- Categorical embedding
- Detailed explanation of Linear Regression – Linear regression assumption
- Cost function
- Gradient Descent
- Linear regression using sklearn
- Model accuracy metrics – RMSE , MSE, MAE
- R2 vs Adjusted R2
- Detailed explanation of Logistics Regression
- Cost function
- Logistics equation
- Model accuracy metrics – Accuracy, ROC, Confusion Matrix, AUC
- k Means Clustering
- DBSCAN Clustering
- PCA
- Support Vector Machines
- Naive Bayes Classifier
- Feature selection techniques
- k Means Clustering
- DBSCAN Clustering
- PCA
- Support Vector Machines
- Naive Bayes Classifier
- Feature selection techniques
- Overfit vs Underfit
- Bias Variance tradeoff
- Grid Search
- Random Search
- Feature Engg examples
- Ridge / Lasso Regression
- SkLearn Pipelines
- SkLearn Imputers
What Our Students Say About Us
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Palin Analytics
Palin Analytics, located in Delhi, is a premier data science institute dedicated to connecting academic learning with industry needs. Through hands-on training, real world projects and mentorship services we equip learners with the necessary skills and confidence needed to solve complex business issues with data.
FAQ's
A top data science institute will feature industry-specific curriculum, experienced trainers, hands-on projects, placement support and certification. Look for institutes which emphasize real world applications like machine learning and business intelligence tools in order to ensure job readiness.
No, 30 is not too late to start a career in data science. Many professionals successfully transition into data roles in their 30s or later. With the right training, projects, and analytical mindset, age is not a barrier in the data science field.
Three months can provide foundational knowledge in data science, while mastery usually takes further practice and project work. An intensive 3-month program with hands-on projects and post-course learning could be just what’s necessary to get you going in your data science career journey.
According to data science’s 80/20 Rule, 80% of insights can often come from 20% of data. It emphasizes focusing on impactful features, variables or datasets to enable efficient analysis and decision-making processes.
Data science is far from dead; rather it is evolving with AI, automation and big data advances enhancing strategic decision-making processes and increasing demand as more organizations rely on data-driven insights across industries.