MODULE 01
01
Python for Data Science
Build the programming base for everything that follows.
- Variables & Types
- Control Flow
- Functions
- OOP Basics
- List / Dict Comprehensions
- Virtual Envs
DATA SCIENCE
Become a Job-Ready Data Scientist.
Learn Python, statistics, machine learning and model building with real datasets, applied projects and placement-focused preparation.
100%
Practical Learning
6–8
Months Program
6+
Major Projects
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)
preds = model.predict(X_test)
print(r2_score(y_test, preds))
🐍
Python
ML
scikit-learn
📊
Pandas
01
Duration
6–8 Months
02
Mode
Classroom + Practical
03
Projects
6+ Real Projects
04
Career
Placement Preparation
COURSE DNA
Everything you need to build and evaluate real models.
One structured path from Python and statistics to machine learning and model deployment.
Python
Build strong programming fundamentals for data work.
NumPy & Pandas
Wrangle, transform and analyse structured data.
Statistics
Apply the statistics behind machine learning.
Data Visualization
Explore data and communicate findings clearly.
Machine Learning
Build supervised and unsupervised models.
Model Evaluation
Measure, compare and tune model performance.
SQL
Pull training data from real databases.
Deployment Basics
Serve a trained model as a simple API.
LEARNING JOURNEY
From raw data to a working model.
Learn in the order the industry actually needs you to learn.
01
FOUNDATION
Python Foundations
Build the Python skills needed for data science work.
02
MATH
Math & Statistics
Understand the statistics that machine learning relies on.
03
DATA
Data Wrangling
Turn messy, real-world data into clean datasets.
04
ANALYSIS
EDA & Visualization
Explore data and form hypotheses before modelling.
05
MODELLING
Machine Learning
Build regression, classification and clustering models.
06
EVALUATION
Model Tuning & Evaluation
Measure performance and improve models responsibly.
07
CAREER
Capstone & Interview Preparation
Prepare for data science interviews and case discussions.
COMPLETE CURRICULUM
What you will actually learn.
MODULE 02
02
NumPy & Pandas
Work efficiently with arrays and tabular data.
- NumPy Arrays
- Vectorisation
- Pandas Series & DataFrames
- Indexing
- GroupBy
- Merging
MODULE 03
03
Statistics & Probability
Learn the maths behind machine learning.
- Descriptive Stats
- Probability
- Distributions
- Sampling
- Confidence Intervals
- Hypothesis Testing
MODULE 04
04
Data Visualization
Explore and explain data visually.
- Chart Selection
- Matplotlib
- Seaborn
- Distributions
- Relationships
- Storytelling
MODULE 05
05
SQL for Data Science
Get training data straight from databases.
- SELECT & Filtering
- Joins
- Aggregations
- Subqueries
- Window Functions
- Exporting Data
MODULE 06
06
Supervised Learning
Predict outcomes from labelled data.
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- KNN
- Naive Bayes
MODULE 07
07
Unsupervised Learning
Find structure in unlabelled data.
- K-Means
- Hierarchical Clustering
- PCA
- Dimensionality Reduction
- Anomaly Detection
- Use Cases
MODULE 08
08
Model Evaluation & Tuning
Make models trustworthy, not just accurate.
- Train / Test / Validation
- Cross Validation
- Confusion Matrix
- Precision & Recall
- ROC / AUC
- Grid Search
MODULE 09
09
ML Project Workflow
Run a project the way a data scientist does.
- Problem Framing
- Data Pipeline
- Baseline Models
- Iteration
- Model Serving Basics
- Documentation
MODULE 10
10
Capstone & Placement
Deliver an end-to-end project and prepare for interviews.
- End-to-End Project
- Presentation
- ML Interview Questions
- SQL Practice
- Resume
- Portfolio
PROJECT BASED LEARNING
Don't just learn. Build.
Your portfolio should prove what you can build, not just what you have studied.
01
EDA
Exploratory Data Analysis Report
Clean a real dataset and produce a full EDA report with visualisations and insights.
02
REGRESSION
Price Prediction Model
Build and evaluate a regression model to predict a continuous value from real data.
03
CLASSIFICATION
Customer Churn Model
Train a classification model to predict churn and evaluate it with proper metrics.
04
CAPSTONE PROJECT
End-to-End Machine Learning Project
Take a problem from data collection and cleaning through modelling, tuning and a simple deployed prediction API.
CAREER PREPARATION
Learn the skills. Prepare to get hired.
Technical skills are only one part of becoming job-ready. We prepare you for the complete hiring process.
✓
Resume Building
Create a data-science-focused resume.
✓
Project Portfolio
Showcase notebooks and end-to-end projects.
✓
Technical Interviews
Statistics, ML concepts, Python and SQL questions.
✓
Case Study Practice
Practice structuring open-ended ML problems.
✓
Mock Interviews
Experience real interview-style sessions.
YOUR CAREER JOURNEY
01 → 05
01
Learn
02
Practice
03
Build Projects
04
Build Portfolio
05
Apply for Jobs
CERTIFICATION
Finish the journey with a portfolio you can show.
Receive your SP IT Academy Data Science certificate after successfully completing the required curriculum and projects.
01
Course Completion Certificate
02
Project-Based Assessment
03
Portfolio Projects
SP IT ACADEMY
PROFESSIONAL CERTIFICATE
This is to certify that
Student Name
has successfully completed the
Data Science
ADMISSION
Ready to start your Data Science journey?
Talk to our counsellor and understand the batch, curriculum, fees and career path.
01
Enquire
Share your basic details with us.
02
Counselling
Discuss your goals with our counsellor.
03
Choose Your Batch
Select the schedule that works for you.
04
Start Learning
Begin your Data Science journey.
SP IT ACADEMY
Your Data Science career starts with one decision.
Learn. Build. Practice. Get ready for the industry.