Learn Python, statistics, data visualization and machine learning with real datasets, predictive modelling projects and placement-focused preparation.
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
df = pd.read_csv("customers.csv")
model = RandomForestClassifier()
model.fit(X_train, y_train)
print(model.score(X_test, y_test))
Python
Pandas
ML
6–8 Months
Classroom + Practical
6+ Real Projects
Placement Preparation
One structured path from Python and statistics to machine learning models you can explain.
Data science combines programming, statistics and machine learning to extract insights from data and predict future outcomes. Our Data Science training uses Python for Data Science from day one, so you learn by working with real datasets.
Data analytics focuses on analysing existing data and reporting what happened and why. Data science goes further, building predictive models with machine learning to forecast what is likely to happen next.
Write clean code to load, analyse and model data.
Work with arrays and DataFrames at scale.
Reason about data with sound statistics.
Fix missing, messy and inconsistent data.
Explore and explain data with charts.
Query and join data from relational databases.
Build regression, classification and clustering models.
Validate, compare and tune models properly.
Learn in the order the industry actually needs you to learn.
Learn Python from the basics with a focus on data work.
Load, reshape and analyse datasets in Python.
Build the statistical intuition behind every model.
Turn raw data into analysis-ready datasets.
Find and communicate patterns with Python charts.
Train and evaluate predictive models with scikit-learn.
Deliver an end-to-end project and prepare for data science interviews.
Python fundamentals, functions and data structures, then NumPy and Pandas for working with real datasets.
Descriptive statistics, distributions, probability, sampling and hypothesis testing for sound, data-driven conclusions.
Handle missing values, duplicates and outliers, and explore patterns in the data before you build any model.
Communicate findings with clear, honest charts using Matplotlib and Seaborn.
Regression, classification and clustering with scikit-learn, plus model evaluation and tuning.
Write Python confidently for data work.
Analyse real datasets in Python.
Pull the data you need from databases.
Learn the statistics behind data science.
Prepare and explore messy, real-world data.
Communicate findings clearly and honestly.
Understand how models learn from data.
Build models that predict outcomes.
Find structure in data and measure model quality.
Deliver an end-to-end data science project and prepare for interviews.
Your portfolio should prove what you can build, not just what you have studied.
Clean real datasets, build and evaluate machine learning models, and present your results the way a data science team would — from exploratory analysis to an end-to-end capstone.
Python
Clean a real-world dataset and publish an EDA notebook with statistics and visual insights.
Regression
Engineer features and train regression models to predict prices, then compare their accuracy.
Classification
Predict which customers are likely to leave and explain the factors that drive churn.
Capstone project
Take a business problem from raw data through cleaning, EDA and modelling to a presented, evaluated solution.
Technical skills are only one part of becoming job-ready. We prepare you for the complete hiring process.
Prepare for roles such as Data Scientist, Junior Data Scientist, ML Analyst, Data Analyst and Business Analyst, with interview preparation covering Python, SQL, statistics and machine learning.
Create a data science-focused resume.
Showcase notebooks, models and case studies on GitHub.
Python, SQL, statistics and machine learning rounds.
Practice framing business problems as data problems.
Experience real interview-style sessions.
Participants of our Data Science Bootcamp receive a certificate after successfully completing and submitting the assigned Data Science project.
Swyam Prabha IT Academy Pvt. Ltd.
This certificate is proudly presented to
for successfully participating in the
Data Science Bootcampand successfully completing and submitting the assigned Data Science Project.
Talk to our counsellor and understand the batch, curriculum, fees and career path.
Share your basic details with us.
Discuss your goals with our counsellor.
Select the schedule that works for you.
Begin your Data Science journey.
Anyone with an interest in numbers and problem-solving can start, including students, working professionals, and career switchers. A background in math, statistics, or programming helps, but isn't mandatory. Our course starts from the basics.
Python programming, statistics and probability, data cleaning and visualization, machine learning, SQL, and real-world projects.
No. We begin with Python fundamentals before moving to advanced topics.
Data Scientist, Machine Learning Engineer, Data Analyst, Business Analyst, and Research Associate are common roles.
We are offering only offline classroom training.
Yes, you'll receive a certificate of completion after successfully finishing the course and projects.
We offer resume building, mock interviews, and job referrals. Please note that we assist with the process, but we cannot guarantee a job.
Yes, you can attend a free demo class before enrolling.
Learn. Build. Practice. Get ready for the industry.