Requirements
- Basic computer skills (file handling, internet, installing software)
- Comfort with numbers (basic math: % , average, ratios; not advanced)
- Basic statistics understanding (mean/median, probability basics)
- Logical thinking (if-else type reasoning, problem-solving mindset)
- Excel basics (filters, sorting, simple formulas) — helpful
- Programming basics (optional): not mandatory, but helps if you know any language
- Laptop/PC: minimum 8GB RAM (16GB recommended), i5/Ryzen5 or similar
- Stable internet for online sessions and project downloads
- Willingness to practice: 5–8 hours/week for assignments + projects
Features
- Live Project-Based Training
- Expert-Led Sessions
- Flexible Learning Options
- Interactive Learning
- Smart Labs with Advanced Equipment
- Unlimited Lab Access
- Comprehensive Study Material
- Globally Recognized Certification
- One-on-One Mentorship
- Career Readiness
- Job Assistance
Target audiences
- 12th pass students (from any stream) who want an IT career start
- College students (BCA/BSc/BTech/BE/BA/Commerce) planning data roles
- Fresh graduates looking for Data Analyst / Jr Data Scientist jobs
- Working professionals who want a career switch into data/AI
- Software developers who want to move into ML/AI projects
- Business/Finance/Marketing professionals who want data-driven skills
- Excel/MIS/Reporting professionals upgrading to analytics + Python
- Professionals preparing for higher studies or research in AI/ML
- Entrepreneurs/startup teams who want to use data for decisions
Data Science Course in Delhi | Data Science Course in Delhi
build machine learning models, and communicate results in a way businesses can use. The training covers the full workflow: data cleaning, analysis, statistics, modeling, and basic deployment.
This Data Science training in Delhi is built for students, working professionals, freshers, and career switchers who want practical job skills. You learn through hands-on labs, guided assignments, and industry-style projects using tools like Python, SQL, Jupyter, Pandas, scikit-learn, Tableau/Power BI, and Git.
By the end of the Data Science Course in Delhi with placement support, learners typically have a portfolio of
projects, a stronger understanding of core data science concepts, and interview-ready confidence for roles like
Data Analyst, Junior Data Scientist, and Machine Learning Engineer (entry-level).
The Data Science Course in Delhi at Ascents Learning is designed around what you actually do on the job:
turning messy data into clear insights and models that support decisions.
You’ll learn how to:
- Collect and prepare data (CSV, Excel, databases, APIs)
- Explore data and find patterns (EDA)
- Apply statistics for better decisions (hypothesis testing, confidence intervals)
- Build and evaluate ML models (regression, classification, clustering)
- Explain outcomes with dashboards and clear storytelling
- Create a portfolio that shows your work
Who Should Enroll
This Data Science training in Delhi fits you if you want a clear learning path and practical outcomes.
Students (UG/PG)
- Want a job-ready skill set before graduation
- Need projects for internships and placements
Freshers
- Want structured training plus portfolio building
- Need interview preparation for analytics/data roles
Working Professionals
- Want to move from operations/IT/support into data roles
- Need real projects and tool exposure (Python, SQL, BI)
Career Switchers
- Want a reliable step-by-step path into data science
- Prefer learning with mentor support and feedback
Helpful background (not required)
- Basic math comfort (percentages, graphs)
- Willingness to practice consistently
Learning Outcomes
After completing the Data Science Course in Delhi, you should be able to do the following with confidence:
Data Handling & Analysis
- Clean data, handle missing values, and fix inconsistent formats
- Use Pandas and NumPy for fast analysis
- Write clear SQL queries (joins, group by, window functions basics)
Statistics & Reasoning
- Summarize distributions and variability
- Use hypothesis testing in practical scenarios (A/B-style thinking)
- Avoid common mistakes like data leakage and wrong metrics
Machine Learning Skills
- Train models using scikit-learn
- Choose the right algorithm for the problem
- Evaluate models using metrics like accuracy, precision/recall, F1, ROC-AUC, RMSE
- Improve performance using feature engineering and hyperparameter tuning
Visualization & Communication
- Build charts with Matplotlib / Plotly
- Create dashboards in Power BI or Tableau
- Explain results in simple business language
Portfolio & Job Readiness
- Build project documentation (problem → approach → results)
- Use Git/GitHub for version control and sharing work
- Prepare for interviews with practical questions and case scenarios
Teaching Methodology
Ascents Learning focuses on learning-by-doing, with structure and feedback.
What the training typically includes:
- Hands-on sessions with live practice
- Weekly assignments to build consistency
- Mini-projects after key modules (EDA, ML, dashboards)
- Capstone project that looks like a real client/business problem
- Mentor reviews to improve code quality and project clarity
- Interview preparation (resume, LinkedIn, mock interviews, HR guidance)
If you’re comparing options, this is one reason many learners shortlist Ascents Learning as a Data Science Training Institute in Delhi: the work output (projects + feedback) is treated as part of the course, not an extra.
Tools & Technologies Covered
This Data Science Course in Delhi uses the standard tools employers expect in entry-level hiring.
Programming & Notebooks
- Python, Jupyter Notebook, Google Colab (optional)
Core Python Libraries
- Pandas, NumPy
- Matplotlib, Plotly (visualization)
- scikit-learn (ML)
Databases & Data Work
- SQL (MySQL/PostgreSQL concepts)
- Data extraction, cleaning, joins, aggregations
Analytics & Dashboards
- Power BI or Tableau (based on track / batch)
- Reporting, KPIs, and interactive dashboards
ML Topics (practical focus)
- Regression, classification, clustering
- Model evaluation, cross-validation
- Feature engineering, pipelines
Basics of Advanced Areas (intro-level)
- Time series basics
- NLP basics (text cleaning, vectorization, simple models)
Deployment & Collaboration (intro-to-practical)
- Git/GitHub
- Basics of APIs (Flask/FastAPI concepts)
- Basics of model packaging and sharing
Certification & Industry Recognition
On completion, learners typically receive a course completion certificate from Ascents Learning that reflects the skills and project work covered in the Data Science training in Delhi track.
What makes a certificate more useful in hiring:
- A linked GitHub portfolio
- Clear project outcomes (problem statement + results)
- Tools listed accurately (Python, SQL, Power BI/Tableau, ML)
Many recruiters care less about the certificate name and more about whether you can explain your project choices and results. This course is built to help you do that.
Career Opportunities After Completion
Completing a Data Science Course in Delhi with placement support can help you qualify for entry-level roles depending on your background,
practice, and project strength.
Common job roles to target:
- Data Analyst
- Junior Data Scientist
- Business Analyst (Data-driven)
- Machine Learning Intern / Trainee
- BI Analyst (Power BI / Tableau)
- Product Analyst (entry-level, analytics-heavy teams)
Typical skills companies screen for:
- Python + SQL fundamentals
- Clean, readable analysis notebooks
- Correct use of evaluation metrics
- Ability to explain insights clearly
- A portfolio with 2–4 strong projects
Why Choose Ascents Learning
If you are searching for the Best Data Science Course in Delhi, it helps to compare based on outputs, not promises.
Here’s what Ascents Learning is known for:
- Practical training with hands-on work and real-world datasets
- Project-based learning (mini projects + capstone)
- Mentor support and structured doubt clearing
- Placement support: resume/LinkedIn help, mock interviews, and job-readiness guidance
- Career mapping: guidance on which role to target (Data Analyst vs Junior Data Scientist, etc.)
- Flexible learning options (online/offline/hybrid batches based on availability)
If your goal is a Data Science Course in Delhi With Placement support, choose a program where you finish with proof of skills: projects, GitHub, and the ability to explain your work.
If you’re looking for a Data Science Course in Delhi that stays practical and job-oriented, talk to the team at
Ascents Learning. You can ask for the latest batch schedule, learning track details, and placement support process.
Call: +91-921-780-6888
Website: www.ascentslearning.com
Curriculum
- 6 Sections
- 202 Lessons
- 22 Weeks
- Module 1: Python FundamentalsIntroduction to Python37
- 1.1Setting up development environment (Anaconda, Jupyter, VS Code)Copy
- 1.2Variables and data types (int, float, string, boolean)Copy
- 1.3Basic operations and expressionsCopy
- 1.4Input/output operationsCopy
- 1.5Practice: Simple calculator, temperature converterCopy
- 1.6Control StructuresCopy
- 1.7Conditional statements (if, elif, else)Copy
- 1.8Loops (for, while)Copy
- 1.9Loop control (break, continue)Copy
- 1.10List comprehensionsCopy
- 1.11Practice: Guess the number game, prime number checkerCopy
- 1.12Data StructuresCopy
- 1.13Lists and list operationsCopy
- 1.14Tuples and their immutabilityCopy
- 1.15Dictionaries and dictionary operationsCopy
- 1.16Sets and set operationsCopy
- 1.17Practice: Contact book app, word frequency counterCopy
- 1.18Functions and ModulesCopy
- 1.19Defining and calling functionsCopy
- 1.20Parameters and return valuesCopy
- 1.21Scope and namespacesCopy
- 1.22Lambda functionsCopy
- 1.23Importing and creating modulesCopy
- 1.24Practice: Custom math library, text analyzerCopy
- 1.25Object-Oriented ProgrammingCopy
- 1.26Classes and objectsCopy
- 1.27Attributes and methodsCopy
- 1.28Inheritance and polymorphismCopy
- 1.29Encapsulation and abstractionCopy
- 1.30Practice: Bank account system, simple inventory managementCopy
- 1.31Advanced Python ConceptsCopy
- 1.32Exception handling (try, except, finally)Copy
- 1.33File operations (read, write, append)Copy
- 1.34Regular expressionsCopy
- 1.35Decorators and generatorsCopy
- 1.36Virtual environments and package management (pip, conda)Copy
- 1.37Practice: Log parser, CSV data processorCopy
- Module 2: SQL and Database FundamentalsIntroduction to Databases80
- 2.1Database concepts and typesCopy
- 2.2Relational database fundamentalsCopy
- 2.3SQL basics (CREATE, INSERT, SELECT)Copy
- 2.4Database design principlesCopy
- 2.5Setting up a database (PostgreSQL/SQLite)Copy
- 2.6Practice: Creating a student database schemaCopy
- 2.7Advanced SQL OperationsCopy
- 2.8JOIN operations (INNER, LEFT, RIGHT, FULL)Copy
- 2.9Filtering and sorting (WHERE, ORDER BY)Copy
- 2.10Aggregation functions (COUNT, SUM, AVG, MIN, MAX)Copy
- 2.11Grouping data (GROUP BY, HAVING)Copy
- 2.12Subqueries and CTEsCopy
- 2.13Indexes and optimizationCopy
- 2.14Practice: Complex queries on an e-commerce databaseCopy
- 2.15Database Integration with PythonCopy
- 2.16Connecting to databases from PythonCopy
- 2.17SQLAlchemy ORMCopy
- 2.18CRUD operations through PythonCopy
- 2.19Transactions and connection poolingCopy
- 2.20Practice: Building a data access layer for an applicationCopy
- 2.21NumPyCopy
- 2.22NumPy FundamentalsCopy
- 2.23Arrays and array creationCopy
- 2.24Array indexing and slicingCopy
- 2.25Array operations and broadcastingCopy
- 2.26Universal functions (ufuncs)Copy
- 2.27Practice: Matrix operations, image processing basicsCopy
- 2.28Advanced NumPyCopy
- 2.29Reshaping and stacking arraysCopy
- 2.30Broadcasting rulesCopy
- 2.31Vectorized operationsCopy
- 2.32Random number generationCopy
- 2.33Linear algebra operationsCopy
- 2.34Practice: Implementing simple ML algorithms with NumPyCopy
- 2.35PandasCopy
- 2.36Pandas FundamentalsCopy
- 2.37Series and DataFrame objectsCopy
- 2.38Reading/writing data (CSV, Excel, SQL)Copy
- 2.39Indexing and selection (loc, iloc)Copy
- 2.40Handling missing dataCopy
- 2.41Practice: Data cleaning for a messy datasetCopy
- 2.42Data Manipulation with PandasCopy
- 2.43Data transformation (apply, map)Copy
- 2.44Merging, joining, and concatenatingCopy
- 2.45Grouping and aggregationCopy
- 2.46Pivot tables and cross-tabulationCopy
- 2.47Practice: Customer purchase analysisCopy
- 2.48Time Series Analysis with PandasCopy
- 2.49Date/time functionalityCopy
- 2.50Resampling and frequency conversionCopy
- 2.51Rolling window calculationsCopy
- 2.52Time zone handlingCopy
- 2.53Practice: Stock market data analysisCopy
- 2.54Data VisualizationCopy
- 2.55Matplotlib FundamentalsCopy
- 2.56Figure and Axes objectsCopy
- 2.57Line plots, scatter plots, bar chartsCopy
- 2.58Customizing plots (colors, labels, legends)Copy
- 2.59Saving and displaying plotsCopy
- 2.60Practice: Visualizing economic indicatorsCopy
- 2.61Advanced MatplotlibCopy
- 2.62Subplots and layoutsCopy
- 2.633D plottingCopy
- 2.64AnimationsCopy
- 2.65Custom visualizationsCopy
- 2.66Practice: Creating a dashboard of COVID-19 dataCopy
- 2.67SeabornCopy
- 2.68Statistical visualizationsCopy
- 2.69Distribution plots (histograms, KDE)Copy
- 2.70Categorical plots (box plots, violin plots)Copy
- 2.71Regression plotsCopy
- 2.72Customizing Seaborn plotsCopy
- 2.73Practice: Analyzing and visualizing survey dataCopy
- 2.74PlotlyCopy
- 2.75Interactive visualizationsCopy
- 2.76Plotly Express basicsCopy
- 2.77Advanced Plotly graphsCopy
- 2.78Dashboards with DashCopy
- 2.79Embedding visualizations in web applicationsCopy
- 2.80Practice: Building an interactive stock market dashboardCopy
- Module 3: ML Statistics for BeginnersIntroduction: Role of statistics in ML, descriptive vs. inferential stats. Descriptive Statistics: Mean, median, variance, skewness, kurtosis. Probability Basics: Bayes' theorem, normal, binomial, Poisson distributions. Inferential Statistics: Sampling, hypothesis testing (Z-test, T-test, Chi-square). Correlation & Regression: Pearson correlation, linear regression, R² score. Hands-on in Python: NumPy, Pandas, SciPy, Seaborn, and statsmodels.70
- 3.1Machine Learning FundamentalsCopy
- 3.2Introduction to Machine LearningCopy
- 3.3Types of machine learning (supervised, unsupervised, reinforcement)Copy
- 3.4The ML workflowCopy
- 3.5Training and testing dataCopy
- 3.6Model evaluation basicsCopy
- 3.7Feature engineering overviewCopy
- 3.8Practice: Implementing a simple linear regression from scratchCopy
- 3.9Scikit-learn BasicsCopy
- 3.10Introduction to scikit-learn APICopy
- 3.11Data preprocessing (StandardScaler, MinMaxScaler)Copy
- 3.12Train-test splitCopy
- 3.13Cross-validationCopy
- 3.14Pipeline constructionCopy
- 3.15Practice: End-to-end ML workflow implementationCopy
- 3.16Supervised LearningCopy
- 3.17Linear ModelsCopy
- 3.18Linear regression (simple and multiple)Copy
- 3.19Regularization techniques (Ridge, Lasso)Copy
- 3.20Logistic regressionCopy
- 3.21Polynomial featuresCopy
- 3.22Evaluation metrics for regression (MSE, RMSE, MAE, R²)Copy
- 3.23Evaluation metrics for classification (accuracy, precision, recall, F1)Copy
- 3.24Practice: Credit scoring modelCopy
- 3.25Decision Trees and Ensemble MethodsCopy
- 3.26Decision tree algorithmCopy
- 3.27Entropy and information gainCopy
- 3.28Overfitting and pruningCopy
- 3.29Random forestsCopy
- 3.30Feature importanceCopy
- 3.31Gradient boosting (XGBoost, LightGBM)Copy
- 3.32Model stacking and blendingCopy
- 3.33Practice: Customer churn predictionCopy
- 3.34Support Vector MachinesCopy
- 3.35Linear SVMCopy
- 3.36Kernel trickCopy
- 3.37SVM hyperparametersCopy
- 3.38Multi-class SVMCopy
- 3.39Practice: Handwritten digit recognitionCopy
- 3.40K-Nearest NeighborsCopy
- 3.41Distance metricsCopy
- 3.42KNN for classification and regressionCopy
- 3.43Choosing K valueCopy
- 3.44KNN limitations and optimizationsCopy
- 3.45Practice: Image classification with KNNCopy
- 3.46Naive BayesCopy
- 3.47Bayes theoremCopy
- 3.48Gaussian, Multinomial, and Bernoulli Naive BayesCopy
- 3.49Applications in text classificationCopy
- 3.50Practice: Spam detectionCopy
- 3.51Unsupervised LearningCopy
- 3.52Clustering AlgorithmsCopy
- 3.53K-means clusteringCopy
- 3.54Hierarchical clusteringCopy
- 3.55DBSCANCopy
- 3.56Gaussian mixture modelsCopy
- 3.57Evaluating clustering performanceCopy
- 3.58Practice: Customer segmentationCopy
- 3.59Dimensionality ReductionCopy
- 3.60Principal Component Analysis (PCA)Copy
- 3.61t-SNECopy
- 3.62UMAPCopy
- 3.63Feature selection techniquesCopy
- 3.64Practice: Image compression, visualization of high-dimensional dataCopy
- 3.65Anomaly DetectionCopy
- 3.66Statistical methodsCopy
- 3.67Isolation ForestCopy
- 3.68One-class SVMCopy
- 3.69Autoencoders for anomaly detectionCopy
- 3.70Practice: Fraud detectionCopy
- Module 5: ML Model Deployment with Flask, FastAPI, and Streamlit6
- Module 6: Final Capstone ProjectDevelop an end-to-end solution that integrates multiple technologies:4
- Tools & Technologies Covered5
- 6.1Languages: PythonCopy
- 6.2Libraries & Frameworks: NumPy, Pandas, Matplotlib, Seaborn, NLTK, TensorFlow, PyTorch, Scikit-learn, LangChainCopy
- 6.3Databases: SQLite, MySQL, Vector databases (ChromaDB, FAISS, Pinecone), Graph databases (Neo4j)Copy
- 6.4Visualization: Matplotlib, Seaborn,PlotlyCopy
- 6.5Deployment: FastAPI, Flask,Streamlit)Copy






