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Data ScienceSKILLSPHERE PROGRAM
Data Science
Learn to turn data into useful insights through statistics, Python, machine learning, visualization, and real-world data projects.
ADMISSIONS OPENOFFLINE
DURATION6 Months
LEVELAll Levels
MODEOffline
TOOLS10
HOW WE TEACH
Turn a technical path into practical capability.
Every part of this program is connected to real work: understand the idea, practise with the right tools, then build something you can explain and show.Practice
Work through the actual technologies, modules and workflows in this path.
Build
Turn the learning into projects, outcomes and portfolio-ready evidence.
THE EXPERIENCE BEHIND THE WORK
Learn with energy. Build with people. Enjoy the process.
Technology is easier to learn when ideas are shared, experiments are encouraged, and real projects bring people together. SkillSphere keeps the work practical, collaborative and genuinely enjoyable.01
PROGRAM OVERVIEW
Know the path. Understand the purpose.
A clear view of what this program teaches, how it is structured, and what you can build with it.
PROGRAM DESCRIPTION
Build practical Data Science skills by learning how to collect, clean, explore, visualize, and interpret data. This program takes students from core Python and statistics concepts to exploratory data analysis, machine learning, and practical predictive solutions. Through hands-on exercises and portfolio-focused projects, learners work with real datasets, identify patterns, build meaningful visualizations, and apply machine learning techniques to solve data-driven problems. The program is designed to develop the analytical and technical skills required to work confidently with modern data.
PROGRAM SNAPSHOT
MODULES08
PROJECTS01
OUTCOMES10
CORE TOOLS10
WHAT YOU'LL WORK WITH10 CORE TOOLS
PythonNumPyPandasMatplotlibSeabornSQL
02
CORE TECHNOLOGIES
The tools become part of the skill.
01Python
02NumPy
03Pandas
04Matplotlib
05Seaborn
06SQL
07Statistics
08Scikit-learn
09Jupyter Notebook
10Machine Learning
03
CURRICULUM
A clear sequence from first principles to working practice.
01
MODULE 01
3 WeeksPython for Data Science
Build a practical Python foundation for data analysis, automation, and data science workflows.
Python fundamentalsVariables and data typesConditional statementsLoopsFunctionsLists and dictionariesFile handlingObject-oriented programming basicsNumPyPandasJupyter Notebook
02
MODULE 02
3 WeeksStatistics & Probability for Data Science
Learn the statistical and probability concepts required to understand datasets and make data-driven decisions.
Descriptive statisticsMean median modeVariance and standard deviationProbability fundamentalsProbability distributionsSamplingCorrelationCovarianceHypothesis testingConfidence intervals
03
MODULE 03
3 WeeksSQL & Database Analytics
Learn to retrieve, transform, and analyze structured data using SQL and relational databases.
SQL fundamentalsSELECT queriesFiltering and sortingAggregate functionsGROUP BYHAVINGJoinsSubqueriesCommon table expressionsWindow functionsDatabase concepts
04
MODULE 04
3 WeeksData Cleaning & Exploratory Data Analysis
Learn how to clean messy datasets, identify patterns, detect anomalies, and prepare data for deeper analysis.
Data cleaningMissing valuesDuplicate recordsOutlier detectionData transformationFeature engineeringExploratory data analysisPandasData profilingCorrelation analysis
05
MODULE 05
3 WeeksData Visualization & Storytelling
Turn complex datasets into clear visual insights, dashboards, and data-driven stories.
MatplotlibSeabornChart selectionBar chartsLine chartsScatter plotsHistogramsHeatmapsDashboard designData storytellingInsight communication
06
MODULE 06
4 WeeksMachine Learning for Data Science
Apply machine learning techniques to build predictive models and solve practical data problems.
Machine learning fundamentalsTrain test splitLinear regressionLogistic regressionKNNDecision treesRandom forestClusteringFeature scalingCross validationModel evaluation
07
MODULE 07
3 WeeksAdvanced Data Analytics
Develop deeper analytical skills for discovering trends, patterns, relationships, and actionable business insights.
Advanced exploratory analysisTime series fundamentalsTrend analysisSegmentationPredictive analyticsFeature selectionDimensionality reductionPCAAnomaly detectionBusiness analytics
08
MODULE 08
2 WeeksData Science Projects & Deployment
Bring the complete data science workflow together through portfolio projects, dashboards, APIs, and deployment.
End-to-end data science workflowProject planningData pipelinesModel deployment basicsStreamlitFastAPI basicsGitGitHubDocumentationPortfolio developmentProject presentation
04
PRACTICAL PROJECTS
Build evidence, not just completion.
PROJECT 01
E-Commerce Sales Analysis Dashboard
Analyze an e-commerce sales dataset to identify revenue trends, top-performing products, customer behavior, and regional performance. Clean the data, perform exploratory analysis, create visualizations, and build an interactive dashboard with actionable business insights.
PythonPandasNumPyMatplotlibSeabornSQLStreamlit
05
LEARNING OUTCOMES
What you should be able to do after the path.
01
Understand data science fundamentals
02
Work with Python for data analysis
03
Clean and preprocess datasets
04
Perform exploratory data analysis
05
Apply statistical concepts to real datasets
06
Create meaningful data visualizations
07
Work with SQL for data extraction
08
Build and evaluate machine learning models
09
Communicate data-driven insights
10
Develop portfolio-ready data science projects
ADMISSIONS
Ready to turn this path into real work?
Start a conversation with SkillSphere about Data Science. We can help you understand the path, expectations and next step.
STATUS Admissions open
ELIGIBILITYStudents, graduates, and working professionals interested in data analysis, statistics, and data science.
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