Data Analytics
Beginner
2–4 months
Learn to clean, analyse, and visualise data to answer business questions using Excel, SQL, and BI tools.
View course detailsData & AI · Online / Offline
Data Science
End-to-end data science: statistics, Python, ML models, and how to present insights that drive business decisions.
Duration3–6 months
LevelBeginner to Intermediate
ModeOnline / Offline
LocationDombivli East & Online
About this course
Data Science combines statistics, programming and machine learning to turn raw data into predictions and decisions. This course takes you from data handling in Python through exploratory analysis, statistics and machine learning models, and shows you how to present results clearly to business teams.
Training is available in Online and Offline modes from our Dombivli East centre at Navare Plaza. Batch timings can be discussed during counselling so the schedule fits students and working professionals.
Key highlights
Python-first approach
Use Python, NumPy and Pandas as your everyday data toolkit.
Statistics that matter
Understand the statistical ideas behind every model you build.
Machine learning workflow
Train, evaluate and improve models on real datasets.
Insight storytelling
Communicate findings through visuals and clear summaries.
What you gain
- A working foundation in Python for data science.
- Confidence in exploratory data analysis and basic statistics.
- Hands-on experience building and evaluating machine learning models.
- A portfolio-style project that shows end-to-end thinking.
Course syllabus
8 modules, taught step by step from fundamentals to practice.
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1
Introduction to Data Science
The data science lifecycle, roles in a data team, problem framing, and where data science is used in business.
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2
Python Programming Essentials
Variables, data types, conditions, loops, functions, lists, dictionaries, file handling, and working in Jupyter Notebook.
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3
Data Analysis with NumPy and Pandas
Arrays, DataFrames, indexing, filtering, grouping, merging, handling missing values, and importing data from CSV and Excel.
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4
Data Visualization
Matplotlib and Seaborn charts, choosing the right chart, distributions, relationships, and exploratory data analysis (EDA).
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5
Statistics and Probability
Descriptive statistics, probability basics, distributions, correlation, sampling, hypothesis testing, and confidence intervals.
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6
SQL for Data Science
Querying relational databases with SELECT, joins, grouping, subqueries, and extracting data for analysis.
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7
Machine Learning Foundations
Supervised and unsupervised learning, regression, classification, clustering, train-test split, overfitting, and model evaluation metrics with Scikit-learn.
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8
Capstone Project and Presentation
Define a problem, prepare data, build and compare models, and present results and recommendations.
Learning approach
Concepts
Guided coding
Dataset practice
Model building
Capstone project
Tools & technologies
Py
Python
Core language for data work and modelling.
PN
Pandas & NumPy
Data manipulation and numerical computing.
MS
Matplotlib & Seaborn
Charts and exploratory analysis.
Sc
Scikit-learn
Machine learning algorithms and evaluation.
JN
Jupyter Notebook
Interactive coding and reporting.
Explore potential career paths
Data Scientist (Junior)
Build models and extract insight from data.
Data Analyst
Analyze data and report on business performance.
Machine Learning Trainee
Support model building and experimentation.
Business Analyst
Use data to guide business decisions.
Career paths shown are illustrative roles that use these skills; outcomes depend on your effort, background and the job market.
Why choose CIT?
- Career-focused learning: A curriculum aligned with relevant Data Science skills.
- Practical approach: Exercises and hands-on practice to reinforce concepts.
- Industry-relevant tools: Learn commonly used tools for Data Science.
- Project-based practice: Opportunities to apply concepts to realistic problems, if included in the delivery.
- Learning for different backgrounds: A pathway designed for learners building or expanding their skills.
Who can join?
- Graduates in any stream curious about data
- Freshers building a data career
- Working professionals moving into analytics
- Learners with basic maths or programming interest
Common questions
Can I attend Data Science Online?
Yes. This course is available in both Online (live sessions) and Offline (Navare Plaza classroom) modes.
Do I need prior experience?
This course is listed as Beginner to Intermediate. Our counsellors will confirm the right starting point based on your background.
How do I get the full syllabus, fees and batch dates?
Fees, syllabus details and batch dates are shared during counselling. Call +91 99672 46501 or send an enquiry.
Start Data Science at CIT
Seats and batch timings vary — enquire today for the next available Online or Offline batch.