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RIGHTOUCH ACADEMY · CHANDIGARH

Data Science

Learn Python, SQL, data analysis, visualization and machine learning fundamentals with practical projects at Rightouch Academy Chandigarh.

Duration: 90 Days

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Data Science Course in Chandigarh

Course Overview

Start your journey into the world of data and technology with a practical Data Science Course in Chandigarh designed for students, graduates, working professionals, and aspiring data professionals. Data Science combines programming, statistics, analytics, and machine learning to transform raw data into meaningful insights and business solutions.

This course helps you build essential skills in Python, SQL, data analysis, data visualization, machine learning, artificial intelligence, and statistical concepts. Through practical exercises, assignments, and real-world projects, you can understand how data is collected, processed, analyzed, and used to support better decision-making.

Whether you are a beginner looking to enter the field or a professional aiming to upgrade your technical skills, structured Data Science training can help you develop a strong foundation. You can learn how to work with datasets, identify patterns, create meaningful visualizations, and apply machine learning techniques to practical problems.

Choosing a Data Science Course in Chandigarh with hands-on learning can give you valuable exposure to industry-relevant tools and techniques while building confidence to work on real-world data projects. With consistent practice and a strong understanding of core concepts, you can prepare yourself for further learning and opportunities in the growing fields of data science, analytics, AI, and machine learning.

Why Choose Us

Learn with guided practical assignments, trainer support and a structured syllabus at Rightouch Academy in Chandigarh. Ask about the current batch and learning format to find the right fit.

Syllabus · Data Science

Your learning roadmap

Click a module to explore its topics.

90 Days8 Modules
01MODULE 01Python SQL and data foundations

Topics covered

  1. Python revision
  2. notebooks
  3. environments
  4. NumPy arrays
  5. pandas tables
  6. SQL selection and joins
  7. data types
  8. dataset provenance
  9. reproducibility
  10. target definition

Practical work

Load a dataset and retrieve a related SQL summary.

Assignment

Submit a data dictionary and a reproducible loading notebook.

02MODULE 02Data cleaning and preparation

Topics covered

  1. Missing data
  2. duplicates
  3. inconsistent categories
  4. date parsing
  5. outliers
  6. joins
  7. reshaping
  8. aggregation
  9. validation rules
  10. train test separation
  11. leakage prevention

Practical work

Clean a messy dataset and record each transformation.

Assignment

Submit clean data, quality checks and a log of assumptions.

03MODULE 03Exploratory analysis and visualisation

Topics covered

  1. Distributions
  2. grouped summaries
  3. correlations
  4. chart selection
  5. misleading scales
  6. seaborn or matplotlib
  7. descriptive narratives
  8. subgroup comparisons
  9. uncertainty

Practical work

Explore three questions using appropriate charts and summaries.

Assignment

Submit an EDA notebook that distinguishes patterns from causal claims.

04MODULE 04Statistics and experiment foundations

Topics covered

  1. Mean and variability
  2. probability
  3. sampling
  4. distributions
  5. confidence intervals
  6. hypothesis tests
  7. correlation versus causation
  8. A/B test concepts
  9. practical significance

Practical work

Compare two sample groups and interpret uncertainty.

Assignment

Submit the calculation, assumptions and a plain language conclusion.

05MODULE 05Supervised machine learning

Topics covered

  1. Regression
  2. classification
  3. baseline models
  4. feature encoding
  5. scaling
  6. decision trees
  7. linear models
  8. regularisation
  9. pipelines
  10. cross validation
  11. hyperparameter search

Practical work

Build a baseline and compare two models without leakage.

Assignment

Submit reproducible preprocessing and a justified model comparison.

06MODULE 06Evaluation interpretation and responsible modelling

Topics covered

  1. MAE and RMSE
  2. precision and recall
  3. confusion matrices
  4. class imbalance
  5. thresholds
  6. overfitting
  7. feature importance limits
  8. bias
  9. privacy
  10. error analysis

Practical work

Analyse mistakes and compare metrics by useful subgroups.

Assignment

Submit an evaluation report explaining tradeoffs and unsuitable uses.

07MODULE 07Unsupervised learning and time based data

Topics covered

  1. Clustering
  2. distance and scaling
  3. dimensionality reduction
  4. segmentation validation
  5. time ordering
  6. lag features
  7. temporal validation
  8. forecasting baselines

Practical work

Create customer clusters or a time based forecasting baseline.

Assignment

Submit validation evidence and explain where the approach fails.

08MODULE 08Model delivery and final project

Topics covered

  1. Saving pipelines
  2. batch prediction
  3. API concepts
  4. input validation
  5. reproducible environments
  6. monitoring drift concepts
  7. documentation
  8. model cards
  9. presentation

Practical work

Package the final notebook and prediction workflow.

Assignment

Deliver data lineage, code, results, limitations and a reproducible run guide.

Course topics and batch timings can be updated. Confirm the current syllabus with the academy before admission.

Additional Information

Final project and assessment
Deliver an exploratory analysis and predictive model on an approved dataset with reproducible preprocessing, validation and a model report.

Duration
90 Days
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