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 Ghaziabad | Data Science Training in Ghaziabad
By the end of the Data Science Course in Ghaziabad with placement support, learners typically leave with a portfolio of projects, stronger confidence in analytics and ML fundamentals, and interview readiness for entry-level roles in data and analytics.
Course Overview
The Data Science Course in Ghaziabad at Ascents Learning is designed around real work scenarios: messy data, practical decision-making, and model performance that must be explained to others.
You’ll learn to:
- Collect and prepare data (Excel/CSV, SQL databases, APIs basics)
- Perform EDA (exploratory data analysis) and identify trends
- Apply statistics for business decisions (sampling, hypothesis testing)
- Build ML models (regression, classification, clustering)
- Evaluate models with correct metrics (F1, ROC-AUC, RMSE, MAE)
- Communicate insights using dashboards and storytelling
- Create a portfolio that recruiters can review
Who Should Enroll
This Data Science training in Ghaziabad is a fit if you want a structured path, hands-on practice, and project output you can show.
Students (UG/PG)
- Want internship-ready skills and projects
- Need a clear learning roadmap beyond theory
Freshers
- Want a portfolio + interview preparation
- Need practical exposure to Python, SQL, and dashboards
Working Professionals
- Want to switch into data roles from IT, operations, sales, finance, support, or QA
- Prefer guided training with mentor feedback
Career Switchers
- Want a step-by-step transition plan into analytics/data science
- Need a practical, tool-based approach rather than only lectures
Good to have (not mandatory)
- Comfort with basic math and charts
- Willingness to practice regularly
Learning Outcomes
After completing the Data Science Course in Ghaziabad, you should be able to handle common data tasks end-to-end.
Data Handling & SQL
- Clean and preprocess datasets (missing values, outliers, inconsistent text)
- Use Pandas and NumPy for analysis
- Write SQL queries for analytics: SELECT, WHERE, GROUP BY, JOINS, aggregations, filtering, and window functions (basics)
Statistics & Decision-Making
- Understand distributions and variability
- Use confidence intervals in real situations
- Apply hypothesis testing with correct assumptions
- Avoid common errors (data leakage, wrong metric selection)
Machine Learning (Practical)
- Train models using scikit-learn
- Build regression, classification, and clustering models
- Evaluate with accuracy, precision, recall, F1, ROC-AUC, RMSE, and MAE
- Improve models using feature engineering and tuning
Visualization & Communication
- Create charts in Matplotlib / Plotly
- Build dashboards in Power BI or Tableau
- Explain outputs clearly to non-technical stakeholders
Portfolio & Collaboration
- Use Git/GitHub for version control
- Document projects (problem → approach → results → next steps)
- Prepare a resume and project walkthrough for interviews
Teaching Methodology
Ascents Learning runs the Data Science training in Ghaziabad with a practical structure so learners don’t get stuck at “watched videos but can’t apply.”
Typical learning format:
- Hands-on instructor-led sessions
- Guided labs in Python/SQL
- Weekly assignments for consistency
- Mini projects after major modules (EDA, SQL, ML, dashboards)
- Capstone project (end-to-end)
- Mentor reviews and doubt clearing
- Placement support: resume/LinkedIn optimization, portfolio guidance (GitHub), mock technical + HR interviews
This approach helps learners build output that can be shown in interviews, which matters for anyone targeting a
Data Science Course in Ghaziabad With Placement support.
Tools & Technologies Covered
This Data Science Course in Ghaziabad covers the tools commonly used in analytics and entry-level data science teams.
Programming & Notebooks
- Python
- Jupyter Notebook (and Colab basics)
Data Analysis
- Pandas
- NumPy
Visualization
- Matplotlib
- Plotly
Databases
- SQL (MySQL/PostgreSQL concepts)
Machine Learning
- scikit-learn
- Model evaluation, cross-validation basics
- Pipelines and feature engineering concepts
BI & Reporting
- Power BI or Tableau (dashboards, KPIs, filters, visuals)
Collaboration
- Git
- GitHub
- Basic project structuring and documentation
Intro Modules (optional/intro level)
- NLP basics (text cleaning, vectorization)
- Time series basics (trends, seasonality concepts)
- API basics and deployment concepts (Flask/FastAPI overview)
Certification & Industry Recognition
On completion, learners typically receive a certificate from Ascents Learning that reflects the track covered under the Data Science Course in Ghaziabad.
What makes the certification more useful in hiring:
- Projects that show practical problem solving
- A GitHub portfolio with readable notebooks and code
- Clear documentation and outcomes, not just screenshots
Recruiters usually focus on your project clarity and tool skills. The certificate helps, but the portfolio does the heavy lifting.
Career Opportunities After Completion
After the Data Science Course in Ghaziabad with placement support, many learners aim for entry-level roles based on their background and project quality.
Common roles:
- Data Analyst
- Junior Data Scientist
- Business Analyst (data-focused)
- BI Analyst (Power BI / Tableau)
- Machine Learning Intern / Trainee
- Product Analyst (analytics-heavy teams)
Skills employers commonly check:
- Python + SQL fundamentals
- EDA and data cleaning ability
- Correct metric usage and model evaluation
- Dashboard/reporting basics
- Ability to explain work clearly
- A portfolio of 2–4 relevant projects
Why Choose Ascents Learning
If you’re comparing the Best Data Science Course in Ghaziabad, focus on how much practical output you’ll build during the program.
Ascents Learning is chosen for:
- Practical, hands-on learning with real datasets
- Industry-style projects and a capstone
- Mentor guidance and structured doubt clearing
- Strong focus on tools used in hiring (Python, SQL, Power BI/Tableau, GitHub)
- Placement support: resume, portfolio, and mock interviews
- Flexible learning modes (online/offline/hybrid based on batch availability)
Ascents Learning aims to function as a reliable Data Science Training Institute in Ghaziabad by keeping the training centered on job tasks, not just theory.
If you want a practical Data Science Course in Ghaziabad with structured learning and placement support, connect with Ascents Learning to get batch details, curriculum flow, and guidance on the right learning track. 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)CopyCopy
- 1.2Variables and data types (int, float, string, boolean)CopyCopy
- 1.3Basic operations and expressionsCopyCopy
- 1.4Input/output operationsCopyCopy
- 1.5Practice: Simple calculator, temperature converterCopyCopy
- 1.6Control StructuresCopyCopy
- 1.7Conditional statements (if, elif, else)CopyCopy
- 1.8Loops (for, while)CopyCopy
- 1.9Loop control (break, continue)CopyCopy
- 1.10List comprehensionsCopyCopy
- 1.11Practice: Guess the number game, prime number checkerCopyCopy
- 1.12Data StructuresCopyCopy
- 1.13Lists and list operationsCopyCopy
- 1.14Tuples and their immutabilityCopyCopy
- 1.15Dictionaries and dictionary operationsCopyCopy
- 1.16Sets and set operationsCopyCopy
- 1.17Practice: Contact book app, word frequency counterCopyCopy
- 1.18Functions and ModulesCopyCopy
- 1.19Defining and calling functionsCopyCopy
- 1.20Parameters and return valuesCopyCopy
- 1.21Scope and namespacesCopyCopy
- 1.22Lambda functionsCopyCopy
- 1.23Importing and creating modulesCopyCopy
- 1.24Practice: Custom math library, text analyzerCopyCopy
- 1.25Object-Oriented ProgrammingCopyCopy
- 1.26Classes and objectsCopyCopy
- 1.27Attributes and methodsCopyCopy
- 1.28Inheritance and polymorphismCopyCopy
- 1.29Encapsulation and abstractionCopyCopy
- 1.30Practice: Bank account system, simple inventory managementCopyCopy
- 1.31Advanced Python ConceptsCopyCopy
- 1.32Exception handling (try, except, finally)CopyCopy
- 1.33File operations (read, write, append)CopyCopy
- 1.34Regular expressionsCopyCopy
- 1.35Decorators and generatorsCopyCopy
- 1.36Virtual environments and package management (pip, conda)CopyCopy
- 1.37Practice: Log parser, CSV data processorCopyCopy
- Module 2: SQL and Database FundamentalsIntroduction to Databases80
- 2.1Database concepts and typesCopyCopy
- 2.2Relational database fundamentalsCopyCopy
- 2.3SQL basics (CREATE, INSERT, SELECT)CopyCopy
- 2.4Database design principlesCopyCopy
- 2.5Setting up a database (PostgreSQL/SQLite)CopyCopy
- 2.6Practice: Creating a student database schemaCopyCopy
- 2.7Advanced SQL OperationsCopyCopy
- 2.8JOIN operations (INNER, LEFT, RIGHT, FULL)CopyCopy
- 2.9Filtering and sorting (WHERE, ORDER BY)CopyCopy
- 2.10Aggregation functions (COUNT, SUM, AVG, MIN, MAX)CopyCopy
- 2.11Grouping data (GROUP BY, HAVING)CopyCopy
- 2.12Subqueries and CTEsCopyCopy
- 2.13Indexes and optimizationCopyCopy
- 2.14Practice: Complex queries on an e-commerce databaseCopyCopy
- 2.15Database Integration with PythonCopyCopy
- 2.16Connecting to databases from PythonCopyCopy
- 2.17SQLAlchemy ORMCopyCopy
- 2.18CRUD operations through PythonCopyCopy
- 2.19Transactions and connection poolingCopyCopy
- 2.20Practice: Building a data access layer for an applicationCopyCopy
- 2.21NumPyCopyCopy
- 2.22NumPy FundamentalsCopyCopy
- 2.23Arrays and array creationCopyCopy
- 2.24Array indexing and slicingCopyCopy
- 2.25Array operations and broadcastingCopyCopy
- 2.26Universal functions (ufuncs)CopyCopy
- 2.27Practice: Matrix operations, image processing basicsCopyCopy
- 2.28Advanced NumPyCopyCopy
- 2.29Reshaping and stacking arraysCopyCopy
- 2.30Broadcasting rulesCopyCopy
- 2.31Vectorized operationsCopyCopy
- 2.32Random number generationCopyCopy
- 2.33Linear algebra operationsCopyCopy
- 2.34Practice: Implementing simple ML algorithms with NumPyCopyCopy
- 2.35PandasCopyCopy
- 2.36Pandas FundamentalsCopyCopy
- 2.37Series and DataFrame objectsCopyCopy
- 2.38Reading/writing data (CSV, Excel, SQL)CopyCopy
- 2.39Indexing and selection (loc, iloc)CopyCopy
- 2.40Handling missing dataCopyCopy
- 2.41Practice: Data cleaning for a messy datasetCopyCopy
- 2.42Data Manipulation with PandasCopyCopy
- 2.43Data transformation (apply, map)CopyCopy
- 2.44Merging, joining, and concatenatingCopyCopy
- 2.45Grouping and aggregationCopyCopy
- 2.46Pivot tables and cross-tabulationCopyCopy
- 2.47Practice: Customer purchase analysisCopyCopy
- 2.48Time Series Analysis with PandasCopyCopy
- 2.49Date/time functionalityCopyCopy
- 2.50Resampling and frequency conversionCopyCopy
- 2.51Rolling window calculationsCopyCopy
- 2.52Time zone handlingCopyCopy
- 2.53Practice: Stock market data analysisCopyCopy
- 2.54Data VisualizationCopyCopy
- 2.55Matplotlib FundamentalsCopyCopy
- 2.56Figure and Axes objectsCopyCopy
- 2.57Line plots, scatter plots, bar chartsCopyCopy
- 2.58Customizing plots (colors, labels, legends)CopyCopy
- 2.59Saving and displaying plotsCopyCopy
- 2.60Practice: Visualizing economic indicatorsCopyCopy
- 2.61Advanced MatplotlibCopyCopy
- 2.62Subplots and layoutsCopyCopy
- 2.633D plottingCopyCopy
- 2.64AnimationsCopyCopy
- 2.65Custom visualizationsCopyCopy
- 2.66Practice: Creating a dashboard of COVID-19 dataCopyCopy
- 2.67SeabornCopyCopy
- 2.68Statistical visualizationsCopyCopy
- 2.69Distribution plots (histograms, KDE)CopyCopy
- 2.70Categorical plots (box plots, violin plots)CopyCopy
- 2.71Regression plotsCopyCopy
- 2.72Customizing Seaborn plotsCopyCopy
- 2.73Practice: Analyzing and visualizing survey dataCopyCopy
- 2.74PlotlyCopyCopy
- 2.75Interactive visualizationsCopyCopy
- 2.76Plotly Express basicsCopyCopy
- 2.77Advanced Plotly graphsCopyCopy
- 2.78Dashboards with DashCopyCopy
- 2.79Embedding visualizations in web applicationsCopyCopy
- 2.80Practice: Building an interactive stock market dashboardCopyCopy
- 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 FundamentalsCopyCopy
- 3.2Introduction to Machine LearningCopyCopy
- 3.3Types of machine learning (supervised, unsupervised, reinforcement)CopyCopy
- 3.4The ML workflowCopyCopy
- 3.5Training and testing dataCopyCopy
- 3.6Model evaluation basicsCopyCopy
- 3.7Feature engineering overviewCopyCopy
- 3.8Practice: Implementing a simple linear regression from scratchCopyCopy
- 3.9Scikit-learn BasicsCopyCopy
- 3.10Introduction to scikit-learn APICopyCopy
- 3.11Data preprocessing (StandardScaler, MinMaxScaler)CopyCopy
- 3.12Train-test splitCopyCopy
- 3.13Cross-validationCopyCopy
- 3.14Pipeline constructionCopyCopy
- 3.15Practice: End-to-end ML workflow implementationCopyCopy
- 3.16Supervised LearningCopyCopy
- 3.17Linear ModelsCopyCopy
- 3.18Linear regression (simple and multiple)CopyCopy
- 3.19Regularization techniques (Ridge, Lasso)CopyCopy
- 3.20Logistic regressionCopyCopy
- 3.21Polynomial featuresCopyCopy
- 3.22Evaluation metrics for regression (MSE, RMSE, MAE, R²)CopyCopy
- 3.23Evaluation metrics for classification (accuracy, precision, recall, F1)CopyCopy
- 3.24Practice: Credit scoring modelCopyCopy
- 3.25Decision Trees and Ensemble MethodsCopyCopy
- 3.26Decision tree algorithmCopyCopy
- 3.27Entropy and information gainCopyCopy
- 3.28Overfitting and pruningCopyCopy
- 3.29Random forestsCopyCopy
- 3.30Feature importanceCopyCopy
- 3.31Gradient boosting (XGBoost, LightGBM)CopyCopy
- 3.32Model stacking and blendingCopyCopy
- 3.33Practice: Customer churn predictionCopyCopy
- 3.34Support Vector MachinesCopyCopy
- 3.35Linear SVMCopyCopy
- 3.36Kernel trickCopyCopy
- 3.37SVM hyperparametersCopyCopy
- 3.38Multi-class SVMCopyCopy
- 3.39Practice: Handwritten digit recognitionCopyCopy
- 3.40K-Nearest NeighborsCopyCopy
- 3.41Distance metricsCopyCopy
- 3.42KNN for classification and regressionCopyCopy
- 3.43Choosing K valueCopyCopy
- 3.44KNN limitations and optimizationsCopyCopy
- 3.45Practice: Image classification with KNNCopyCopy
- 3.46Naive BayesCopyCopy
- 3.47Bayes theoremCopyCopy
- 3.48Gaussian, Multinomial, and Bernoulli Naive BayesCopyCopy
- 3.49Applications in text classificationCopyCopy
- 3.50Practice: Spam detectionCopyCopy
- 3.51Unsupervised LearningCopyCopy
- 3.52Clustering AlgorithmsCopyCopy
- 3.53K-means clusteringCopyCopy
- 3.54Hierarchical clusteringCopyCopy
- 3.55DBSCANCopyCopy
- 3.56Gaussian mixture modelsCopyCopy
- 3.57Evaluating clustering performanceCopyCopy
- 3.58Practice: Customer segmentationCopyCopy
- 3.59Dimensionality ReductionCopyCopy
- 3.60Principal Component Analysis (PCA)CopyCopy
- 3.61t-SNECopyCopy
- 3.62UMAPCopyCopy
- 3.63Feature selection techniquesCopyCopy
- 3.64Practice: Image compression, visualization of high-dimensional dataCopyCopy
- 3.65Anomaly DetectionCopyCopy
- 3.66Statistical methodsCopyCopy
- 3.67Isolation ForestCopyCopy
- 3.68One-class SVMCopyCopy
- 3.69Autoencoders for anomaly detectionCopyCopy
- 3.70Practice: Fraud detectionCopyCopy
- 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: PythonCopyCopy
- 6.2Libraries & Frameworks: NumPy, Pandas, Matplotlib, Seaborn, NLTK, TensorFlow, PyTorch, Scikit-learn, LangChainCopyCopy
- 6.3Databases: SQLite, MySQL, Vector databases (ChromaDB, FAISS, Pinecone), Graph databases (Neo4j)CopyCopy
- 6.4Visualization: Matplotlib, Seaborn,PlotlyCopyCopy
- 6.5Deployment: FastAPI, Flask,Streamlit)CopyCopy






