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 Gurgaon | Data Science Training in Gurgaon
analyzing patterns, building machine learning models, and explaining results to teams. The curriculum follows the full workflow from raw data to insights, with a clear focus on hands-on practice.This Data Science training in Gurgaon is designed for students, working professionals, freshers, and career switchers. You learn through instructor-led labs, guided assignments, and project work using tools employers expect, such as Python, SQL, Jupyter Notebook, Pandas, NumPy, scikit-learn, Power BI/Tableau, and GitHub.
By the end of the Data Science Course in Gurgaon with placement support, learners usually have 2–4 portfolio-ready projects, a better grip on statistics and ML basics, and interview readiness for roles in analytics and entry-level data science.
The Data Science Course in Gurgaon at Ascents Learning is built to help you do three things well: work with data
confidently, model problems with machine learning, and communicate results clearly.
You’ll learn how to:
- Prepare datasets from Excel/CSV and databases
- Perform EDA (exploratory data analysis) to find patterns and trends
- Apply statistics in real scenarios (sampling, hypothesis testing, confidence intervals)
- Build ML models for common use cases: regression, classification, and clustering
- Validate models with correct metrics (F1, ROC-AUC, RMSE, MAE)
- Build dashboards and reports using Power BI or Tableau
- Present findings with clear reasoning, not just charts
Who Should Enroll
This Data Science training in Gurgaon works best for learners who want a structured path, regular practice, and measurable output.
Students (UG/PG)
- Want internship projects and job-ready skills
- Need confidence with Python, SQL, and dashboards
Freshers
- Want a portfolio that proves practical ability
- Need interview-focused training on tools and problem solving
Working Professionals
- Want to transition into data roles from IT, operations, finance, sales, support, or QA
- Prefer guided practice and mentor feedback over self-study
Career Switchers
- Want a step-by-step roadmap into analytics/data science
- Need real projects that match hiring expectations
Helpful but not required
- Comfort with basic math and charts
- Willingness to practice consistently
Learning Outcomes
After completing the Data Science Course in Gurgaon, you should be able to handle real tasks from data collection to model evaluation.
Data Handling & SQL
- Clean data (missing values, duplicates, outliers, text standardization)
- Analyze data using Pandas and NumPy
- Write SQL for analytics: joins, aggregations, filtering, grouping, and window functions (basics)
Statistics & Business Reasoning
- Work with distributions, correlation, and variance
- Use confidence intervals and hypothesis testing correctly
- Recognize common analysis mistakes (wrong metric, leakage, bias)
Machine Learning (Job-Focused)
- Train models with scikit-learn
- Use evaluation properly: accuracy, precision, recall, F1, ROC-AUC, RMSE/MAE
- Improve results with feature engineering and tuning basics
- Explain model behavior in simple terms (what changed, why it matters)
Visualization & Communication
- Create visuals in Matplotlib / Plotly
- Build dashboards in Power BI or Tableau
- Turn analysis into clear recommendations for stakeholders
Portfolio & Collaboration
- Use Git/GitHub to manage and share code
- Write project documentation (problem → approach → results → limitations)
- Build interview-ready project walk-throughs
Teaching Methodology
Ascents Learning runs the Data Science Course in Gurgaon with a practical setup so learners build skill through repetition, feedback, and projects.
Typical learning format includes:
- Instructor-led hands-on sessions (Python, SQL, ML, BI)
- Guided labs with real datasets
- Weekly assignments for consistency
- Mini projects after major topics (SQL + EDA + ML + dashboards)
- Capstone project (end-to-end problem solving)
- Mentor feedback on code, approach, and project clarity
- Placement support: resume and LinkedIn improvements, GitHub portfolio structure, mock interviews (technical + HR), and role mapping
This structure is helpful for learners choosing a Data Science Course in Gurgaon With Placement support, because it creates proof of work, not just course completion.
Tools & Technologies Covered
This Data Science training in Gurgaon covers the tools that are commonly used in analytics and entry-level DS roles.
Programming & Notebooks
- Python
- Jupyter Notebook (Colab basics optional)
Core Libraries
- Pandas, NumPy
- Matplotlib, Plotly
Databases
- SQL (MySQL/PostgreSQL concepts)
Machine Learning
- scikit-learn
- Model validation basics (cross-validation, train/test split)
- Feature engineering concepts
- Pipelines basics
BI & Reporting
- Power BI or Tableau (KPIs, visuals, filters, dashboards)
Collaboration
- Git, GitHub
- Project documentation and version control practices
Intro Topics (basic exposure)
- NLP basics (text cleaning, vectorization)
- Time series basics (trend/seasonality concepts)
- API + deployment concepts (Flask/FastAPI overview)
Certification & Industry Recognition
After completing the Data Science Course in Gurgaon, learners typically receive a course completion certificate from Ascents Learning.
What makes the certificate more meaningful for hiring:
- A portfolio of projects with clear outcomes
- A GitHub profile with readable notebooks and code
- The ability to explain model choices and evaluation metrics
In interviews, your project walkthrough usually matters more than the certificate alone, so the course focuses heavily on project clarity.
Career Opportunities After Completion
After the Data Science Course in Gurgaon with placement support, learners often apply for entry-level roles based on their strengths and
project work.
Common roles:
- Data Analyst
- BI Analyst (Power BI / Tableau)
- Junior Data Scientist
- Business Analyst (data-focused)
- Machine Learning Intern / Trainee
- Product Analyst (analytics-heavy teams)
Skills employers often test:
- Python + SQL fundamentals
- Data cleaning and EDA
- Correct metric selection and interpretation
- Basic ML modeling with scikit-learn
- Dashboard/reporting knowledge
- Clear communication and project explanation
Why Choose Ascents Learning
If you’re searching for the Best Data Science Course in Gurgaon, it helps to evaluate the program by outputs: projects, tool confidence, and interview readiness.
Ascents Learning is chosen for:
- Practical training with real datasets and guided labs
- Project-based learning (mini projects + capstone)
- Mentor support and structured doubt clearing
- Strong coverage of hiring tools (Python, SQL, Power BI/Tableau, GitHub)
- Placement support: resume, LinkedIn, mock interviews, and role mapping
- Flexible learning modes (online/offline/hybrid depending on batch)
As a Data Science Training Institute in Gurgaon, Ascents Learning keeps the learning aligned with real job tasks and real evaluation methods.
If you want a practical Data Science Course in Gurgaon with structured training and placement support, connect with Ascents Learning for batch schedules, curriculum details, and the right learning track based on your goals.
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)CopyCopyCopy
- 1.2Variables and data types (int, float, string, boolean)CopyCopyCopy
- 1.3Basic operations and expressionsCopyCopyCopy
- 1.4Input/output operationsCopyCopyCopy
- 1.5Practice: Simple calculator, temperature converterCopyCopyCopy
- 1.6Control StructuresCopyCopyCopy
- 1.7Conditional statements (if, elif, else)CopyCopyCopy
- 1.8Loops (for, while)CopyCopyCopy
- 1.9Loop control (break, continue)CopyCopyCopy
- 1.10List comprehensionsCopyCopyCopy
- 1.11Practice: Guess the number game, prime number checkerCopyCopyCopy
- 1.12Data StructuresCopyCopyCopy
- 1.13Lists and list operationsCopyCopyCopy
- 1.14Tuples and their immutabilityCopyCopyCopy
- 1.15Dictionaries and dictionary operationsCopyCopyCopy
- 1.16Sets and set operationsCopyCopyCopy
- 1.17Practice: Contact book app, word frequency counterCopyCopyCopy
- 1.18Functions and ModulesCopyCopyCopy
- 1.19Defining and calling functionsCopyCopyCopy
- 1.20Parameters and return valuesCopyCopyCopy
- 1.21Scope and namespacesCopyCopyCopy
- 1.22Lambda functionsCopyCopyCopy
- 1.23Importing and creating modulesCopyCopyCopy
- 1.24Practice: Custom math library, text analyzerCopyCopyCopy
- 1.25Object-Oriented ProgrammingCopyCopyCopy
- 1.26Classes and objectsCopyCopyCopy
- 1.27Attributes and methodsCopyCopyCopy
- 1.28Inheritance and polymorphismCopyCopyCopy
- 1.29Encapsulation and abstractionCopyCopyCopy
- 1.30Practice: Bank account system, simple inventory managementCopyCopyCopy
- 1.31Advanced Python ConceptsCopyCopyCopy
- 1.32Exception handling (try, except, finally)CopyCopyCopy
- 1.33File operations (read, write, append)CopyCopyCopy
- 1.34Regular expressionsCopyCopyCopy
- 1.35Decorators and generatorsCopyCopyCopy
- 1.36Virtual environments and package management (pip, conda)CopyCopyCopy
- 1.37Practice: Log parser, CSV data processorCopyCopyCopy
- Module 2: SQL and Database FundamentalsIntroduction to Databases80
- 2.1Database concepts and typesCopyCopyCopy
- 2.2Relational database fundamentalsCopyCopyCopy
- 2.3SQL basics (CREATE, INSERT, SELECT)CopyCopyCopy
- 2.4Database design principlesCopyCopyCopy
- 2.5Setting up a database (PostgreSQL/SQLite)CopyCopyCopy
- 2.6Practice: Creating a student database schemaCopyCopyCopy
- 2.7Advanced SQL OperationsCopyCopyCopy
- 2.8JOIN operations (INNER, LEFT, RIGHT, FULL)CopyCopyCopy
- 2.9Filtering and sorting (WHERE, ORDER BY)CopyCopyCopy
- 2.10Aggregation functions (COUNT, SUM, AVG, MIN, MAX)CopyCopyCopy
- 2.11Grouping data (GROUP BY, HAVING)CopyCopyCopy
- 2.12Subqueries and CTEsCopyCopyCopy
- 2.13Indexes and optimizationCopyCopyCopy
- 2.14Practice: Complex queries on an e-commerce databaseCopyCopyCopy
- 2.15Database Integration with PythonCopyCopyCopy
- 2.16Connecting to databases from PythonCopyCopyCopy
- 2.17SQLAlchemy ORMCopyCopyCopy
- 2.18CRUD operations through PythonCopyCopyCopy
- 2.19Transactions and connection poolingCopyCopyCopy
- 2.20Practice: Building a data access layer for an applicationCopyCopyCopy
- 2.21NumPyCopyCopyCopy
- 2.22NumPy FundamentalsCopyCopyCopy
- 2.23Arrays and array creationCopyCopyCopy
- 2.24Array indexing and slicingCopyCopyCopy
- 2.25Array operations and broadcastingCopyCopyCopy
- 2.26Universal functions (ufuncs)CopyCopyCopy
- 2.27Practice: Matrix operations, image processing basicsCopyCopyCopy
- 2.28Advanced NumPyCopyCopyCopy
- 2.29Reshaping and stacking arraysCopyCopyCopy
- 2.30Broadcasting rulesCopyCopyCopy
- 2.31Vectorized operationsCopyCopyCopy
- 2.32Random number generationCopyCopyCopy
- 2.33Linear algebra operationsCopyCopyCopy
- 2.34Practice: Implementing simple ML algorithms with NumPyCopyCopyCopy
- 2.35PandasCopyCopyCopy
- 2.36Pandas FundamentalsCopyCopyCopy
- 2.37Series and DataFrame objectsCopyCopyCopy
- 2.38Reading/writing data (CSV, Excel, SQL)CopyCopyCopy
- 2.39Indexing and selection (loc, iloc)CopyCopyCopy
- 2.40Handling missing dataCopyCopyCopy
- 2.41Practice: Data cleaning for a messy datasetCopyCopyCopy
- 2.42Data Manipulation with PandasCopyCopyCopy
- 2.43Data transformation (apply, map)CopyCopyCopy
- 2.44Merging, joining, and concatenatingCopyCopyCopy
- 2.45Grouping and aggregationCopyCopyCopy
- 2.46Pivot tables and cross-tabulationCopyCopyCopy
- 2.47Practice: Customer purchase analysisCopyCopyCopy
- 2.48Time Series Analysis with PandasCopyCopyCopy
- 2.49Date/time functionalityCopyCopyCopy
- 2.50Resampling and frequency conversionCopyCopyCopy
- 2.51Rolling window calculationsCopyCopyCopy
- 2.52Time zone handlingCopyCopyCopy
- 2.53Practice: Stock market data analysisCopyCopyCopy
- 2.54Data VisualizationCopyCopyCopy
- 2.55Matplotlib FundamentalsCopyCopyCopy
- 2.56Figure and Axes objectsCopyCopyCopy
- 2.57Line plots, scatter plots, bar chartsCopyCopyCopy
- 2.58Customizing plots (colors, labels, legends)CopyCopyCopy
- 2.59Saving and displaying plotsCopyCopyCopy
- 2.60Practice: Visualizing economic indicatorsCopyCopyCopy
- 2.61Advanced MatplotlibCopyCopyCopy
- 2.62Subplots and layoutsCopyCopyCopy
- 2.633D plottingCopyCopyCopy
- 2.64AnimationsCopyCopyCopy
- 2.65Custom visualizationsCopyCopyCopy
- 2.66Practice: Creating a dashboard of COVID-19 dataCopyCopyCopy
- 2.67SeabornCopyCopyCopy
- 2.68Statistical visualizationsCopyCopyCopy
- 2.69Distribution plots (histograms, KDE)CopyCopyCopy
- 2.70Categorical plots (box plots, violin plots)CopyCopyCopy
- 2.71Regression plotsCopyCopyCopy
- 2.72Customizing Seaborn plotsCopyCopyCopy
- 2.73Practice: Analyzing and visualizing survey dataCopyCopyCopy
- 2.74PlotlyCopyCopyCopy
- 2.75Interactive visualizationsCopyCopyCopy
- 2.76Plotly Express basicsCopyCopyCopy
- 2.77Advanced Plotly graphsCopyCopyCopy
- 2.78Dashboards with DashCopyCopyCopy
- 2.79Embedding visualizations in web applicationsCopyCopyCopy
- 2.80Practice: Building an interactive stock market dashboardCopyCopyCopy
- 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 FundamentalsCopyCopyCopy
- 3.2Introduction to Machine LearningCopyCopyCopy
- 3.3Types of machine learning (supervised, unsupervised, reinforcement)CopyCopyCopy
- 3.4The ML workflowCopyCopyCopy
- 3.5Training and testing dataCopyCopyCopy
- 3.6Model evaluation basicsCopyCopyCopy
- 3.7Feature engineering overviewCopyCopyCopy
- 3.8Practice: Implementing a simple linear regression from scratchCopyCopyCopy
- 3.9Scikit-learn BasicsCopyCopyCopy
- 3.10Introduction to scikit-learn APICopyCopyCopy
- 3.11Data preprocessing (StandardScaler, MinMaxScaler)CopyCopyCopy
- 3.12Train-test splitCopyCopyCopy
- 3.13Cross-validationCopyCopyCopy
- 3.14Pipeline constructionCopyCopyCopy
- 3.15Practice: End-to-end ML workflow implementationCopyCopyCopy
- 3.16Supervised LearningCopyCopyCopy
- 3.17Linear ModelsCopyCopyCopy
- 3.18Linear regression (simple and multiple)CopyCopyCopy
- 3.19Regularization techniques (Ridge, Lasso)CopyCopyCopy
- 3.20Logistic regressionCopyCopyCopy
- 3.21Polynomial featuresCopyCopyCopy
- 3.22Evaluation metrics for regression (MSE, RMSE, MAE, R²)CopyCopyCopy
- 3.23Evaluation metrics for classification (accuracy, precision, recall, F1)CopyCopyCopy
- 3.24Practice: Credit scoring modelCopyCopyCopy
- 3.25Decision Trees and Ensemble MethodsCopyCopyCopy
- 3.26Decision tree algorithmCopyCopyCopy
- 3.27Entropy and information gainCopyCopyCopy
- 3.28Overfitting and pruningCopyCopyCopy
- 3.29Random forestsCopyCopyCopy
- 3.30Feature importanceCopyCopyCopy
- 3.31Gradient boosting (XGBoost, LightGBM)CopyCopyCopy
- 3.32Model stacking and blendingCopyCopyCopy
- 3.33Practice: Customer churn predictionCopyCopyCopy
- 3.34Support Vector MachinesCopyCopyCopy
- 3.35Linear SVMCopyCopyCopy
- 3.36Kernel trickCopyCopyCopy
- 3.37SVM hyperparametersCopyCopyCopy
- 3.38Multi-class SVMCopyCopyCopy
- 3.39Practice: Handwritten digit recognitionCopyCopyCopy
- 3.40K-Nearest NeighborsCopyCopyCopy
- 3.41Distance metricsCopyCopyCopy
- 3.42KNN for classification and regressionCopyCopyCopy
- 3.43Choosing K valueCopyCopyCopy
- 3.44KNN limitations and optimizationsCopyCopyCopy
- 3.45Practice: Image classification with KNNCopyCopyCopy
- 3.46Naive BayesCopyCopyCopy
- 3.47Bayes theoremCopyCopyCopy
- 3.48Gaussian, Multinomial, and Bernoulli Naive BayesCopyCopyCopy
- 3.49Applications in text classificationCopyCopyCopy
- 3.50Practice: Spam detectionCopyCopyCopy
- 3.51Unsupervised LearningCopyCopyCopy
- 3.52Clustering AlgorithmsCopyCopyCopy
- 3.53K-means clusteringCopyCopyCopy
- 3.54Hierarchical clusteringCopyCopyCopy
- 3.55DBSCANCopyCopyCopy
- 3.56Gaussian mixture modelsCopyCopyCopy
- 3.57Evaluating clustering performanceCopyCopyCopy
- 3.58Practice: Customer segmentationCopyCopyCopy
- 3.59Dimensionality ReductionCopyCopyCopy
- 3.60Principal Component Analysis (PCA)CopyCopyCopy
- 3.61t-SNECopyCopyCopy
- 3.62UMAPCopyCopyCopy
- 3.63Feature selection techniquesCopyCopyCopy
- 3.64Practice: Image compression, visualization of high-dimensional dataCopyCopyCopy
- 3.65Anomaly DetectionCopyCopyCopy
- 3.66Statistical methodsCopyCopyCopy
- 3.67Isolation ForestCopyCopyCopy
- 3.68One-class SVMCopyCopyCopy
- 3.69Autoencoders for anomaly detectionCopyCopyCopy
- 3.70Practice: Fraud detectionCopyCopyCopy
- 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: PythonCopyCopyCopy
- 6.2Libraries & Frameworks: NumPy, Pandas, Matplotlib, Seaborn, NLTK, TensorFlow, PyTorch, Scikit-learn, LangChainCopyCopyCopy
- 6.3Databases: SQLite, MySQL, Vector databases (ChromaDB, FAISS, Pinecone), Graph databases (Neo4j)CopyCopyCopy
- 6.4Visualization: Matplotlib, Seaborn,PlotlyCopyCopyCopy
- 6.5Deployment: FastAPI, Flask,Streamlit)CopyCopyCopy






