PG Diploma in Data Science

Data Science is a fast-expanding subject that integrates statistical analysis, programming, and domain knowledge to extract valuable insights and create data-driven choices.



6 Months

Recommended 8-10 Hours/Week

Dec 20, 2023

Start Date

No Cost EMI*

Flexible EMI Options

Course Includes

Data Science Training

Data Science training provides individuals with the skills and knowledge necessary to excel in data analysis and interpretation. The training typically covers topics such as statistics, programming languages, machine learning algorithms, data visualisation, and data manipulation techniques. Participants learn to extract insights from complex datasets, perform exploratory data analysis, build predictive models, and communicate findings effectively. Practical hands-on exercises and projects are often included to enhance real-world applications. By completing Data Science training, individuals gain a strong foundation in data analysis, enabling them to pursue diverse career opportunities in industries that rely on data-driven decision-making.

  • Introduction to python
  • Variables and strings
  • Lists, list manipulation and comprehensions, tuples and dictionaries.
  • Conditional operators, If-else statements and logical operators.
  • Loops-while, for loops, nested loops.
  • Indexing, slicing.
  • Exploratory data analysis
  • Numpy using python
  • Pandas introduction and why is it necessary for data science.
  • Series and Data frames.
  • Importing data through csv and json files.
  • Analysing the data with pandas- handling missing data, descriptive statistics, concatenating, merging and string manipulation.
  • Statistics in data science and why is it important.
  • Population and sample- with a use case example.
  • Parameter and statistic- with a use case example.
  • univariate and bi-variate analysis.
  • measures of central tendencies.
  • central limit theorem.
  • measures of dispersion.
  • skewness and kurtosis.
  • Box plot and outlier detection.
  • Correlation and covariance.
  • What is probability
  • Sample space, random variables.
  • types of probabilities.
  • Dependent and independent events.
  • Probability distribution function.
  • Binomial, Bernoulli and poisson distributions.
  • normal/Gaussian distribution.
  • Data types- qualitative and quantitative.
  • Data collection techniques.
  • Sampling techniques.
  • Convenience sampling, random sampling.
  • Stratified, systematic and cluster sampling.
  • What is data pre-processing?
  • What is exploratory data analysis?
  • Uni-variate analysis.
  • Bi-variate analysis.


  • What is data visualisation and why is It important?
  • Plotting- line plot, scatter plot, box plot, bar charts, heat maps, count plots etc.
  • Matplotlib
  • seaborn
  • Advanced matplotlib and seaborn- with practical implementation.
  • Advantages and disadvantages of data visualisation.
  • Introduction to machine learning.
  • Supervised and unsupervised learning.
  • Regression and classification, difference between them.


  • Simple linear regression and multiple linear regression.
  • Estimating the coefficients.
  • ERROR METRICS – R-squared, adjusted r squared, MSE, RMSE.


  • Lasso and ridge regression.
  • Difference between the above with a use case example.


  • Logistic regression
  • Logit and sigmoid functions.
  • Why logistic regression is a classification technique.
  • Difference between regression and classification.
  • Evaluation metrics for classification models- Accuracy, precision, recall, f1-score.



  • Overview on Decision trees.
  • Nodes, root node and terminal nodes.
  • Regression trees and classification trees.
  • Advantages and disadvantages of tree based models.
  • Gini index, entropy, information gain.
  • Overfitting and pruning.


  • Principle of naïve bayes classifier.
  • Bayes theorem.
  • Posterior probability
  • Prior probability of class
  • Likelihood
  • Re-sampling techniques.
  • Cross-validation, K-fold cross validation
  • Bias-variance
  • Bias variance trade off.

Ensemble methods in tree- based models: 

  • What is ensemble learning?
  • Bootstrap aggregation.
  • Bagging and boosting.
  • Random forest-What is random forest and how does it work?
  • Difference between decision trees and random forest
  • What is boosting?
  • Boosting algorithms- adaboost, Gradient boosting- XGboost.

K-nearest neighbors: 

  • K-nearest neighbor algorithm
  • Why is it called lazy learners algorithm?
  • How does the KNN algorithm work?
  • Curse of dimensionality.
  • Pro’s and cons of KNN.


  • Maximum marginal classifier.
  • Hard margin and soft margin.
  • Support vector classifiers.
  • Kernel trick.
  • Polynomial and radial.
  • Hyper parameters svm.

PROJECT: Backorders dataset where in student has to use ML models and predict the risk of backorders.

  • What is unsupervised learning?
  • Difference between unsupervised and supervised learning.


  • Introduction to dimensionality reduction and why Is it needed?
  • Principal components
  • eigen values, eigen vectors and orthogonality.


  • Centroids
  • Optimal value of ‘k’ selection.
  • Elbow method.
  • Use case In real time.

Hierarchical clustering: 

  • Divisive and Agglomerative clustering.
  • Application of clustering
  • Association rules: Market basket analysis
  • Apriori algorithm:
  • Metrics- support, confidence, lift.
  • Frequent itemset.
  • Use-case example.

MATERIALS: COLAB notebook with python code and PPT.

  • Introduction neural networks, perceptron.
  • Activation functions- Relu, sigmoid, softmax, tanh.
  • Gradient descent
  • Learning rate
  • Optimization techniques and functions.
  • Back propagation and vanishing gradients.
  • Introduction to tensorflow and keras libraries.
  • Real time use case examples and practical hands-on.

PROJECT: Custom dataset will be provided along with problem statement from Kaggle.

  • How to work with images -introduction.
  • Pixels and resolutions.
  • Reshaping, resizing, changing to grayscale images.
  • Practical hands-on the above topics.
  • Convolutional neural networks- pooling and convolution of images concept.
  • CNN architecture
  • OBJECT DETECTION- region of proposal, YOLO and SSD.
  • Overview of NLP.
  • Difference between text mining and text processing.
  • Pre-processing, tokenization, stop words removal, normalization, stemming and lemmatization.
  • Bag of words, continuous bag of words, TF-IDF.
  • Language probabilistic models, n-gram model.
  • Hands-on NLTK
  • WORD EMBEDDINGS- word2vec, glove, Named entity recognition.
  • Recurrent neural networks, Bi-directional rnn’s.
  • LSTM- Long short term memory
  • Hands-on practice.
  • Applications of NLP.

PROJECT: Emotion dataset from KAGGLE will be provided along with the problem statement.

MATERIALS: PPT’S and GOOGLE COLAB notebook with code while practicing will be provided for further reference.


  • Intro to SQL
  • Downloading workbench and configuring, connecting my SQL server
  • What is database management system?
  • Why is it essential?
  • What is relational database management system?
  • SQL commands
  • What is a table?
  • Entity relationship model


  • MY SQL basics
  • Keys
  • Clauses in MY SQL
  • Create, update and delete.
  • Modify the table
  • Select and where clause
  • Distinct clause
  • Order by, group by
  • Having clause
  • ISNULL and NOTNULL clause
  • LIMIT and OFFSET clause


  • Operators in sql
  • Arithmetic operators
  • Wildcard operators
  • AND, OR operators in SQL
  • Concatenation operator
  • MINUS, DIVISION operator in SQL
  • NOT operator
  • BETWEEN operator in SQL


  • Mathematical functions in SQL
  • DATE functions in SQL
  • String functions in SQL
  • Numeric functions in SQL
  • Aggregate functions in SQL


  • JOINS in SQL
  • Inner join vs outer join
  • Sub queries
  • Nested queries
  • Co-related queries.


  • SQL with python
  • Above topics will be covered using a dataset in python


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Data Science - Hands on Projects

Data Science Certification Course

Show the world that you have pursued one of the best in the industry.

The duration to complete a Data Science certification can vary depending on the program’s depth and the individual’s learning pace. It can range from a few weeks for accelerated programs to several months for comprehensive courses.

After completing Data Science certification training, you can apply for various job roles such as Data Scientist, Data Analyst, Machine Learning Engineer, Business Analyst, Data Engineer, AI Specialist, and Predictive Analyst, among others.

Many Data Science certification programs include a certification exam as a final assessment. This exam tests the knowledge and skills acquired during the training and validates the individual’s proficiency in Data Science concepts and techniques.

Data Science
Data Science Certification Course

A Data Science certification is a credential awarded to individuals who have successfully completed a program or course focused on Data Science concepts, techniques, and tools. It verifies their proficiency and knowledge in the field.

A Data Science certification can enhance your career prospects by demonstrating your expertise in Data Science to potential employers. It validates your skills and knowledge, making you more competitive in the job market.

The choice of Data Science certification depends on your career goals, existing skills, and the industry you wish to work in. Popular certifications include those from IBM, Microsoft, SAS, and Google. Research various certifications to find the one that aligns with your needs and interests.

The duration of Data Science certifications can vary significantly. Some certifications can be completed within a few weeks, while others may take several months. It depends on the depth and breadth of the curriculum and the individual’s learning pace.

Data Science certifications from reputable organizations are generally recognized and valued by employers. However, it’s essential to research the industry and specific employers to understand their preferences and requirements regarding certifications.

Yes, it is possible to learn Data Science without a certification. Many individuals acquire knowledge and skills through self-study, online courses, or university programs. While certifications can provide formal recognition, practical experience and a strong portfolio of projects can also showcase your abilities to potential employers.

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