Data Science Course in Patiala
Data Science Training in Patiala with Industry-Focused Instruction
Since its emergence, data science training programs in Patiala have become one of the most in-demand career tracks, offering opportunities in fields as diverse as IT, finance, healthcare, marketing and e-commerce. With businesses increasingly turning to data-driven decision making strategies for their operations, Data Scientists are in high demand across industries.
- 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
Who Should Attend Data Science Course in Delhi
Our course is ideal for: Students and fresh graduates; IT professionals, business analysts, finance and marketing specialists as well as finance analysts.
Who Should Join Data Science Careers
No prior coding experience is necessary – basic mathematics knowledge and an analytical mindset will do.
Want to Discuss Your Roadmap to Become a Data Scientist in Patiala?
Our specialists can create a bespoke roadmap based on your career goals – from skill enhancement and project guidance, all the way through placement preparation. Give Us A Call Back Now!
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 of Patiala offers expert data science training. As a premier data science institute, Palin aims to bridge the gap between academic learning and industry requirements by offering hands-on training, real world projects and mentoring that gives learners confidence and the ability to solve complex business challenges using data.
FAQ's
A top Data Science course in Patiala should include industry-focused curriculum, hands-on projects, machine learning training, placement assistance and certification services. When looking for such programs in Patiala look for programs with emphasis on real world applications as well as tools like Python, SQL Server Tableau or AI technologies for job readiness.
Fees will depend on course length, learning format (online/offline), features included such as placement support and certifications as well as flexible payment options and value-driven pricing – something Palin Analytics Patiala makes accessible through flexible payment options and value pricing models.
All types of students, fresh graduates, working professionals and career switchers from any background are welcome to enroll in this Data Science course in Patiala; no prior coding experience is necessary and basic mathematics knowledge and analytical thinking is helpful but not mandatory for enrollment.
Yes, Palin Analytics provides placement assistance such as resume writing, mock interviews, career mentoring sessions and interview preparation sessions to help students secure roles in data science, machine learning and analytics domains.
This course covers Python, R, statistics, machine learning (ML), deep learning (DL), NLP data visualizations using Tableau, SQL as well as Hadoop/Spark technologies with database management to give students industry ready projects as capstone projects for real world application.