Data Science Course in Gurgaon
The Data Science course in Gurgaon is one of the hottest career options today with endless potential across industries. A data Science course in Gurgaon equips professionals with knowledge and skills necessary to harnessing information for better decision-making and problem solving, with numerous well-recognized institutes offering tailored Data Science programs designed specifically to meet industry demands.
- 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
In our Data Science course in Gurgaon, you will acquire a comprehensive skill set for turning data into actionable insights. Beginning with programming fundamentals in Python and R, as well as essential data analysis and visualization techniques. From there you’ll move onto machine learning concepts including unsupervised and supervised learning techniques as well as advanced topics like deep learning and natural language processing. In addition to SQL for database management and Tableau for creating impactful reports; through hands-on projects you will develop the experience needed to successfully enter the data industry!
Who can go for Data Science course in Gurgaon
Our Gurgaon Data Science course is ideal for students, fresh graduates, IT professionals, business analysts and marketing or finance professionals seeking a career in data. Featuring advanced data science techniques such as machine learning as well as transition/upskill opportunities with no strict prerequisites – just basic mathematics skills and an analytical mindset are sufficient. Get on your journey toward becoming a data expert now!
Want to discuss your roadmap to be a Data Scientist in Gurgaon?
Are You Planning on Becoming a Data Scientist in Gurgaon? Ready to Take the Next Step in Your Career?
Our experts can assist in devising a custom roadmap towards Data Science success – learn the skills necessary, take advantage of career transition guidance, learn from anywhere, share certificates with classmates and prepare a personalized plan of success tailored specifically for you in this industry.
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
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Palin Analytics
Palin Analytics, Gurgaon’s premier data science institute, was established with a mission of connecting academic knowledge with industry demands and molding the next generation of data professionals. We believe in practical hands-on training as an essential method for mastering the complex world of data, equipping our students with all of the skills, tools and confidence required to transform raw data into powerful business solutions.
FAQ's
Look for courses covering Python, Machine Learning (ML), statistics and Tableau with hands-on projects and job placement assistance.
Rs5-8 LPA depending on skills, portfolio and company. With experience comes significant salary increases.
To choose correctly between these two options for data analysis and business insights and programming/software systems respectively.
No longer just about data analysis; instead it has evolved with AI and automation, making data analysis an essential element of decision-making.
Yes. In three months you should be able to develop foundational skills in SQL, Python and data pipelines – but becoming job-ready usually takes much more than three months of practice, projects and hands-on experience.
Data science creates insights from data, while AI develops intelligent systems. DS acts as the base for specialization within AI fields while AI makes their systems.