Become Job-Ready in Data Science, Python & Machine Learning
Build Python, SQL and ML projects that you can explain in interviews, with placement preparation from resume to mock interviews.
Explore our courses, student placement records and placement preparation process. Placement outcomes depend on eligibility, skills and employer selection.
Roles students can target
These entry-level roles connect the course skills to real interview opportunities.
Data Scientist
Machine Learning Engineer
Python Data Analyst
AI/ML Associate
BI Analyst
Junior Data Engineer
What you'll use in every class
Industry tools covered in training, projects, and placement preparation.
Learn the skills companies ask in interviews
Each module builds toward projects, resume confidence, and interview answers.
Python Programming for Data Science
- Python syntax, variables, data types, operators, and control flow
- Functions, modules, packages, virtual environments, and pip
- OOP in Python — classes, inheritance, dunder methods, and decorators
- List comprehensions, generators, lambda, map, filter, and itertools
- File handling — reading CSV, JSON, and Excel for data loading
- Jupyter Notebook — running experiments, markdown, and sharing findings
Statistics & Probability for Data Analysis
- Descriptive statistics — mean, median, mode, variance, standard deviation
- Probability basics — distributions, Bayes theorem, and Central Limit Theorem
- Hypothesis testing — t-test, chi-square test, ANOVA, and p-value interpretation
- Correlation and covariance — understanding feature relationships
- Outlier detection and handling skewed or imbalanced data distributions
SQL for Data Scientists
- SELECT queries — filtering, ordering, grouping, and aggregate functions
- Joins — INNER, LEFT, RIGHT, FULL OUTER with real business datasets
- Subqueries, CTEs, and window functions — ROW_NUMBER, RANK, LAG, LEAD
- Data cleaning in SQL — handling NULLs, deduplication, and type casting
- Connecting MySQL to Python — feeding query results into Pandas DataFrames
Data Wrangling with Pandas & NumPy
- NumPy arrays — vectorized operations, broadcasting, and matrix arithmetic
- Pandas Series and DataFrames — loading, indexing, slicing, and filtering
- Data cleaning — handling missing values, duplicates, and inconsistent formats
- Merging, grouping, pivoting, and reshaping datasets for analysis
- Time series data — resampling, rolling windows, and date-based operations
Data Visualization & Exploratory Data Analysis
- Matplotlib — line, bar, scatter, histogram, subplots, and chart customization
- Seaborn — heatmaps, boxplots, pairplots, and statistical chart defaults
- Plotly — interactive charts and dashboards inside Jupyter Notebook
- EDA workflow — identifying patterns, outliers, correlations, and business questions
- Storytelling with data — presenting insights clearly to non-technical stakeholders
Machine Learning Algorithms & Projects
- Supervised learning — Linear Regression, Logistic Regression, Decision Trees
- Ensemble methods — Random Forest, Gradient Boosting, XGBoost
- Model evaluation — accuracy, precision, recall, F1-score, confusion matrix, ROC-AUC
- Feature engineering, selection, and hyperparameter tuning with GridSearchCV
- Unsupervised learning — K-Means clustering, PCA for dimensionality reduction
- Model saving with Pickle/Joblib and serving predictions via a Flask endpoint
Projects students can show during interviews
Telecom Subscriber Churn Prediction
Classification model on telecom data — specific domain makes it stand out in interviews.
Retail Store Weekly Demand Forecast
Time-series regression — useful for explaining prediction and trend analysis.
Bank Loan Default Risk Analysis
Finance domain classification — precision/recall focus shows ML evaluation depth.
Hospital Patient Readmission Likelihood Model
Healthcare ML — healthcare domain projects get immediate attention in interviews.
Placement preparation
Practice technical interviews, prepare a project portfolio and discuss current opportunities with our counsellors. Employer selection and job offers depend on each candidate and vacancy.
Watch mock interviews & practice sessions
See how real interviews go — preparation, answers, and feedback included.
What Is AI & Data Science? | Explained for Beginners
Crack Your First Python Interview | Data Science & SQL
Python Interview for Freshers | Core Python Mock Interview
9-Year-Old Python Genius — Are You Keeping Up?
One Interview Can Change Your Entire Career!
Make Your Resume Perfect with ChatGPT — Get Shortlisted!
What Is AI & Data Science? | Explained for Beginners
Crack Your First Python Interview | Data Science & SQL
Python Interview for Freshers | Core Python Mock Interview
9-Year-Old Python Genius — Are You Keeping Up?
One Interview Can Change Your Entire Career!
Make Your Resume Perfect with ChatGPT — Get Shortlisted!
From basics to placement preparation
Python + SQL Foundation
Start from practical coding, queries, and data handling.
Analytics + Visualization
Clean data, explore patterns, and create reports.
Machine Learning Projects
Build models, evaluate them, and improve predictions.
Portfolio + Placement Prep
Resume, mock interviews, GitHub and project explanation.
Good fit for these students
- BCA, BCS, BE, BTech, BSc, MCA, MBA students
- Freshers who want data jobs
- Working professionals moving into AI/ML
- Non-IT students ready to learn Python
Beginner career path
Your first class is free.
Attend a demo session before you decide. No obligation, no fees — just learning.
Data Science or Data Analytics?
Choose Data Science if you want Python, machine learning, prediction models, and AI-related roles.
Common student doubts
Short answers for course, placement, and project expectations.
The listed course starts with Python basics and progresses to statistics, SQL, analysis and machine learning. Beginners should be prepared to practise programming and quantitative reasoning. Confirm prerequisites and the pace of the current batch before enrolling.
Pay depends on the role, employer, location, experience and offer terms. A verified course-specific salary range is not currently published. Compare the compensation stated in current vacancies and written offers, including fixed pay, variable pay and any internship stipend.
Data science may suit beginners who enjoy programming, statistics and working with data. Start with Python, SQL and data analysis, then practise evaluating models and explaining results. Compare the prerequisites of advertised roles with your skills; some roles require experience or further study.
Data Analytics focuses on interpreting existing data using Excel, SQL, and Power BI to answer business questions and build dashboards. Data Science goes further — it involves building predictive models using Python, machine learning algorithms, and statistics to forecast outcomes and automate decisions. Choose Data Analytics for reporting roles; choose Data Science for ML and AI-related jobs.
Yes. You will build 4 portfolio-ready ML projects — telecom churn prediction, demand forecasting, loan default risk analysis, and hospital patient readmission model — across different domains so you can explain them confidently in any interview.
Check current vacancies for the role and the employer requirements in each listing. A verified employer-by-course hiring list is not currently published here. Ask for approved, current references before relying on a claimed hiring relationship; a company name alone is not evidence of an active vacancy.
Yes. Placement support includes resume preparation built around your ML projects, mock interviews for Python, SQL, statistics, and model explanation, GitHub portfolio setup, and dedicated placement calls with interview question preparation.
Interview invitations and job offers depend on open roles, employer eligibility requirements, project quality and interview performance. A verified course-specific hiring timeframe is not currently published. Use course completion to review your portfolio and application readiness rather than as a promised interview date.
Any graduation degree is acceptable — BCA, BSc, BTech, BE, MCA, MBA, or a commerce background. The course starts from Python basics with zero assumed knowledge.
Yes. A laptop with at least 8GB RAM is recommended. All software — Anaconda, Jupyter, Python, VS Code — is free to install. No paid tools are required.
Batches are kept small (max 12 students) to ensure individual attention. Both weekday and weekend batches are available. Contact us at +91 8888809416 for the current schedule.
The course lists a completion certificate alongside portfolio work. Confirm the completion requirements and certificate details with a counsellor. A course completion certificate documents training; employers assess qualifications, projects and interview performance according to their own criteria.