Data Analytics Course in Patiala
Learn Data Analytics in Patiala with Industry-Focused Training
Patiala’s Data Analytics courses have seen unprecedented demand as businesses increasingly turn to data-driven decision making across industries such as IT, finance, healthcare, marketing, retail and e-commerce. A Data Analytics Course equips learners with practical skills necessary for collecting, cleaning, analyzing and interpreting data in order to enhance business performance and strategy.
- English
- English, Hindi
Upcoming Batch Weekdays!!!
Starting from Upcoming Weekend!
09:00 am – 1:00 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 in data analytics course Patiala in palin
- Connection, Navigation, Data Sources
- Data Extraction, Data Prep
- Data Pre-Processing
- Exploratory Data Analytics
- Data Wrangling
- Data Cleansing
- Data Munging
- Statistics For Business Analytics
- Data Analytics
- Machine Learning
- Data Visualization
- Python for everyone
What all topics we will cover in Data Analytics Course in Palin Analytics
At our Data Analytics course in Patiala, you will acquire in-depth knowledge of turning raw data into actionable insights. Starting with Excel, Python and SQL for data handling before moving onto statistical analysis using Tableau and Power BI for visualisation purposes.
Who Can Go for a Data Analytics Course in Patiala?
- This course is ideal for: Students and new graduates.
- Working professionals, from marketing and finance executives alike.
- Business Professionals to switch careers in analytics
- No prior coding knowledge is required for starting out; just basic mathematics skills, logical thinking and an interest in data are enough to get the ball rolling.
Want to Discuss Your Roadmap to Become a Data Analyst in Patiala?
- Our Experts Create Customized Roadmaps Our experts craft personalized roadmaps including:Skill Development Planning
- Career Transition guidance.
- Flexible Learning formats / Certification prepration, Interview support and resume support
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.
what is Course Content of data Analytics Course in Palin Analytics Patiala
Module 1: Introduction to Excel for Analytics
Overview of Excel as an analytics tool
Importance of Excel in data analysis
Excel interface and basic navigation
Module 2: Essential Excel Functions for Data Analysis
Understanding and using basic functions (SUM, AVERAGE, COUNT)
Working with mathematical and statistical functions
Text functions for data cleaning and manipulation
Logical functions (IF, AND, OR)
Module 3: Data Import and Cleaning in Excel
Importing data from various sources (CSV, Excel, databases)
Data cleaning techniques and best practices
Handling missing data
Module 4: Data Visualization in Excel
Creating basic charts (bar charts, line charts, pie charts)
Formatting and customizing charts
Using sparklines for trend analysis
Module 5: PivotTables and PivotCharts
Introduction to PivotTables
Creating PivotTables for data summarization
Building PivotCharts for visual analysis
Module 6: Advanced Excel Functions for Analytics
VLOOKUP and HLOOKUP for data retrieval
INDEX and MATCH functions
Advanced IF statements and nested functions
Using array formulas
Module 7: Data Analysis with Excel Tables
Introduction to Excel Tables
Sorting and filtering data in tables
Using structured references
Module 1: Introduction to Databases and SQL
Understanding Databases
Definition and types of databases
Relational databases
Non-relational databases
Introduction to SQL
What is SQL?
SQL history and evolution
Importance of SQL in the industry
Module 2: Basic SQL Commands
SELECT statement
Retrieving data from a single table
Filtering data using WHERE clause
Sorting data using ORDER BY clause
INSERT, UPDATE, DELETE statements
Adding, modifying, and deleting data
Data integrity considerations
Module 3: Data Types and Operators
Common SQL data types
Working with text, numeric, and date data
Arithmetic and comparison operators
Module 4: Advanced Querying
JOIN operations
INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN
Cross join and self-join
Subqueries
Nested queries
Correlated subqueries
Set operations
UNION, INTERSECT, EXCEPT
Module 5: Data Filtering and Aggregation
GROUP BY clause
HAVING clause
Aggregate functions (SUM, AVG, COUNT, MIN, MAX)
Module 6: Data Modification
Transactions
COMMIT and ROLLBACK statements
Constraints (PRIMARY KEY, FOREIGN KEY, UNIQUE, CHECK)
Module 7: Views and Indexes
Creating and managing views
Indexes and their impact on performance
Module 8: Stored Procedures and Functions
Creating stored procedures
Input and output parameters
User-defined functions
Module 9: Database Security
User roles and permissions
GRANT and REVOKE statements
Securing sensitive data
Module 10: Best Practices and Optimization
Writing efficient queries
Indexing strategies
Query optimization techniques
Module 11: Introduction to NoSQL Databases
Overview of NoSQL databases
Contrasting SQL and NoSQL
Module 12: Case Studies and Real-world Applications
Analyzing real-world scenarios
Building practical solutions with SQL
Introduction to Programming
Basics of programming logic
Understanding algorithms and flowcharts
Overview of Python as a programming language
Setting Up Python Environment
Installing Python
Working with Python IDEsÂ
(Integrated Development Environments)
Writing and executing the first Python script
Python Basics
Variables and data types
Basic operations (arithmetic, comparison, logical)
Input and output (print, input)
Control Flow
Conditional statements (if, elif, else)
Loops (for, while)
Break and continue statements
Functions in Python
Defining functions
Parameters and return values
Scope and lifetime of variables
Lists and Tuples
Creating and manipulating lists
Slicing and indexing
Working with tuples
Dictionaries and Sets
Understanding dictionaries
Operations on sets
Use cases for dictionaries and sets
Module 4: File Handling
Reading and Writing Files
Opening and closing files
Reading from and writing to files
Working with different file formats (text, CSV)
Module 5: Error Handling and Modules
Error Handling
Introduction to exceptions
Try, except, finally blocks
Handling different types of errors
Python Libraries for Data Analytics
NumPy for numerical operations
Pandas for data manipulation and analysis
Matplotlib and Seaborn for data visualization
Data Cleaning and Preprocessing
Handling missing data
Data imputation techniques
Data normalization and standardization
Exploratory Data Analysis (EDA)
Descriptive Statistics
Measures of central tendency and dispersion
Skewness and kurtosis
Correlation and covariance
Module 1: Foundations of Statistics
Overview of StatisticsDefinition and scope of statistics
Descriptive vs. inferential statistics
Data Types and Measurement Scales
Categorical vs. numerical data
Nominal, ordinal, interval, and ratio scales
Descriptive Statistics
Measures of central tendency (mean, median, mode)
Measures of variability (range, variance, standard deviation)
Module 2: Probability Theory
Introduction to Probability
Basic probability concepts
Probability rules and laws
Probability Distributions
Discrete and continuous distributions
Normal distribution and its properties
Sampling Distributions
Central Limit Theorem
Standard error and confidence intervals
Module 3: Inferential Statistics
Hypothesis Testing
Formulating hypotheses
Type I and Type II errors
Parametric Tests
t-tests for means
Analysis of variance (ANOVA)
Non-parametric Tests
Mann-Whitney U test
Kruskal-Wallis test
Module 4: Correlation and Regression
Correlation Analysis
Pearson correlation coefficient
Spearman rank correlation
Regression Analysis
Simple linear regression
Multiple linear regression
Module 5: Bayesian Statistics
Bayesian Concepts
Bayes’ Theorem
Prior, likelihood, and posterior probabilities
Bayesian Inference
Bayesian hypothesis testing
Bayesian modeling
Module 1: Introduction to Tableau
Overview of Tableau
Understanding the Tableau interface
Connecting to data sources
Data source considerations and best practices
Module 2: Data Preparation and Cleaning in Tableau
Importing and cleaning data
Managing metadata
Joins and relationships
Data blending
Module 3: Basic Visualization Techniques
Creating basic charts (bar charts, line charts, scatter plots)
Building maps and geographic visualizations
Using size and color in visualizations
Dual-axis charts and combo charts
Module 4: Intermediate Visualization Techniques
Working with calculated fields
Building hierarchies
Creating sets and groups
Trend lines and reference lines
Module 5: Advanced Visualization Techniques
Advanced chart types (treemaps, heat maps, box plots)
Dashboard design principles
Storytelling with data
Interactive dashboards and actions
What Our Students Say About Us
About Palin Analytics
Palin Analytics now offering expert training in Patiala is an established analytics training institute focused on connecting academic education with industry requirements through hands-on instruction, live projects, and personalized mentorship to enable our learners to develop successful data analytics careers.
FAQ's
Any student, recent graduates, working professionals, and career switchers from any background are welcome to enroll. No prior technical experience is necessary – basic mathematics knowledge, logical thinking skills and an interest in data analysis suffice as entry requirements to start learning data analytics.
Yes, our Data Analytics course in Patiala does provide placement assistance in terms of resume writing, mock interviews, career mentoring sessions and portfolio guidance sessions in addition to interview preparation sessions – with an ultimate goal to secure roles in data analytics, business intelligence and reporting fields for our students.
Our course covers Excel, Python, SQL, Tableau and Power BI along with statistical tools, data visualization techniques, dashboarding tools and database management systems in order to provide complete industry readiness.
Yes, they are tailored for novice learners and begin with essential concepts like Excel and Python basics before moving on to more complex analytics concepts. Step-by-step learning ensures even non-technical students can grasp these ideas without difficulty.
Yes, our program includes real-life industry projects and case studies that give students hands-on experience with data cleansing, analysis, dashboard creation and business reporting – giving them practical skills they’ll need for future careers.