Accelerate your career in Data Science with the exclusive Data ScienceAnalytics Program. Experience world-class Data Science training by an industry leader on the most in-demand Data Science Skills. Gain hands-on exposure to key technologies including R, Python, Machine Learning. Become an expert Data Science professional today.

The PGP-DS (Post Graduate Program in Data Science) gives you wide coverage to main ideas and techniques from Python, Exploratory Data Analysis to Machine Learning, Deep Learning, and more. Practical labs and assignment work bring these ideas to life with our instructors and assistants to supervise you with the path. Equip your career with this commended PG Program in Data Science with Careerera and the team. Our post graduate program in data science is the leading data science course in the education field

Data Science and Artificial intelligence have transformed the world completely. Organizations around the world are leveraging artificial intelligence to avoid repetitive tasks and improve customer experience. Robots are taking on the world by storm and are continuously building intelligence comparable to human brains. Artificial Intelligence and Machine Learning are the highest paying jobs in the world. As per a recent estimate, more than 90% of the companies will use artificial intelligence in one way or the other to build or enhance their products and services. These companies are looking for people who are skilled in data science and AI. Unfortunately, the industry is facing an acute shortage of highly skilled people to fill the void. Fortunately, Careerera decided to be a part of the solution and has launched its PGP program in data science so that people can take advantage of our services and avail our data science graduate certificate online.

Welcome to Careerera 12 months Post Graduate Program(PGP) in Data Science, Machine Learning, and Neural Networks

Program Features

1) 12-Month Online Program
2) 6-Month Internship (Inclusive in the program)
3)  Dedicated Placement Team
4) Live Online Sessions
5) Capstone Project
6) Student Faculty Ratio 15:1
7) 25+ Industry Graded projects

Why Data Science?

1) Build/grow your career in data science roles and companies
2) An Academic pedigree that is best in the industry and can help you get a job
3) Learn through live online sessions and hands-on learning.
4) Learn data science tools and technologies sought after by leading companies

On completing this program, you will :

1) Be well versed in analytics tools and technologies such as Python, Tableau, SQL
2) Apply industry-relevant machine learning techniques such as Regression, Predictive Modelling, Clustering, Time Series, Forecasting, Classification, etc.
3) Structure a business problem into an analytics framework using statistics and data modelling
4) Be well versed in Deep learning, Natural Language Processing (NLP).
5) Present yourself as an ideal candidate who possesses a post graduate diploma in data science for analyst, data engineer, and data scientist roles within leading analytics companies

Why Careerera For Data Science?

1) 12 Month Online Program
2)  100 % Guaranteed Placement(Subject To Terms and Conditions)
3) Capstone Project
4) Highly Experienced Faculties
5) Comprehensive Curriculum




  • Hello World
  • Variables
  • Basic Arithmetic & logical operators (int, float)
  • Data Types - numbers, boolean & strings
  • Concat, Subset, Position, length etc.
  • If-else, loops
  • Logic Flowcharts (Intuitive understanding of code flow)
  • Pseudo Code
  • Basic Programming syntax
  • List, Tuples, Sets & Dictionaries ​
  • Default functions
  • Default methods
  • Intro​ to Conditional statements (if-else, elif), Nested Conditional in Python ​
  • Intro​ to Basic For, While Loops, Break in Python​
  • Convert pseudo codes from Day 1 into programs using Loops and if-else. ​
  • List Comprehension​
  • Use cases vs Loops
  • Write Programs including both loops and If-else
  • Practice list comprehensions
  • Lab Exercises
  • Exploring commonly used built in functions (min, max, sort etc.)
  • Programming user defined functions
  • Working with functions with and without arguments
  • Functions with return items
  • Understanding lambda functions
  • Overview of map, reduce and filter functions
  • Reading the Data
  • Cleaning the Data
  • Data Visualization in Python
  • Summary statistics (mean, median, mode, variance, standard deviation)
  • Seaborn
  • Matplotlib
  • Population VS sample
  • Univariate and Multivariate statistics
  • Types of variables – Categorical and Continuous
  • Coefficient of correlations, Skewness and kurtosis
  • Probability distribution
  • Normal distribution
  • Poisson's distribution
  • Bayes’ theorem
  • Central limit theorem
  • Hypothesis testing
  • One Sample T-Test
  • Anova and Chi-Square
  • Introduction to DBMS
  • An Introduction to Relational Database
  • Concepts and SQL Accessing
  • Data Servers MYSQL/RDBMS Concepts
  • Extraction, Transformation and Loading (“ETL”) Processes
  • Retrieve data from Single Tables-(use of SELECT Statement) and the power of WHERE and ORDER by Clause. Retrieve and Transform data from multiple Tables using JOINS and Unions
  • Introduction to Views Working with Aggregate functions, grouping and summarizing Records Writing Sub queries

Machine Learning Techniques (Supervised Learning+ Unsupervised Learning)- 6 Weeks

  • Looking at regression through the perspective of machine learning
  • Accuracy scores as a metric of model performance
  • Measuring the importance of individual variables in a regression model
  • Review - testing for individual significance vs joint significance
  • Using the adjusted R^2 to compare model with different number of independent variables
  • Approaches to feature selection
  • Forward and backward selection
  • Parameter tuning and Model evaluation
  • Extending linear regression
  • Data transformations and normalization
  • Log transformation of dependent and independent variables
  • Case study: -
  • Dealing with categorical independent variables
  • One hot encoding vs dummy variable regression
  • Case study on linear regression
  • Modelling probabilistic dependent variables
  • The sigmoid function and odds ratio
  • The concept of logit
  • The failure of OLS in estimating parameters for a logistic regression
  • Introduction to the concept of Maximum likelihood estimation
  • Advantages of the maximum likelihood approach
  • Modelling a logistic regression problem with a case study
  • Making predictions and evaluating parameters
  • Classification Problems – Examples.
  • Binary classification vs Multi class classification.
  • Decision trees – Simple decision trees. Visualizing decision trees and nodes and splits.
  • Working of the Decision tree algorithm.
  • Importance and usage of Entropy and Gini index.
  • Manually calculating entropy using gini formula and working out how to split decision nodes
  • Evaluating decision tree models.
  • Accuracy metrics – precision, recall and confusion matrix
  • Interpretation for accuracy metric.
  • Building a a robust decision tree model. k-fold cross validation - Advantages against simple train test split.
  • CART - Extending decision trees to regressing problems.
  • Advantages of using CART.
  • The Bayes theorem. Prior probability.
  • The Gaussian NAÏVE’S BAYES Classifier.
  • Assumptions of the Naive Bayes Classifier.
  • Functioning of the Naïve’s Bayes algorithm.
  • Evaluating the model  - Precision, Recall, Accuracy metrics and  k-fold cross validation
  • ROC Curve and AUC for binary classification for Naive Bayes.
  • Extending Bayesian Classification for multiclass classification
  • What is Unsupervised learning?
  • The two major Unsupervised Learning problems - Dimensionality reduction and clustering.
  • Clustering algorithms.
  • The different approaches to clustering – Heirarchical and K means clustering.
  • Heirarchical clustering - The concept of agglomerative and divisive clustering.
  • Agglomerative Clustering – Working of the basic algorithms.
  • Distance matrix - Interpreting dendograms.
  • Choosing the threshold to determine the optimum number of clusters.
  • Case Study on Agglomerative clustering
  • The K-means algorithm.
  • Measures of distance – Euclidean, Manhattan and Minowski distance.
  • The concept of within cluster sums of squares.
  • Using the elbow plot to select optimum number of cluster’s.
  • Case study on k-means clustering.
  • Comparison of k means and agglomerative approaches to clustering.
  • Noise in the data and dimensional reduction.
  • Capturing Variance - The concept of a principal components.
  • Assumptions in using PCA.
  • The working of the PCA algorithm.
  • Eigen vectors and orthogonality of principal components.
  • What is complexity curve?
  • Advantages of using PCA.
  • Bulid a model using Principal components and comparing with normal model. What is the difference?
  • Putting it all together.
  • The relationship between unsupervised and supervised learning.
  • Case study on Dimensionality reduction followed by a supervised learning model.
  • Case study on Clustering followed by classification model.
  • Bagging
  • Boosting
  • Bagging & Boosting Examples
  • Introduction to Visualization, Rules of Visualization
  • Data Types, Sources, Connections, Loading, Reshaping
  • Data Aggregation
  • Working with Continuous and Discrete Data
  • Using Filters
  • Using Calculated Fields and parameters
  • Creating Tables and Charts
  • Building Dash Boards and story boards
  • Sharing Your Work and Publishing for wider audience
  • What is Time Series?
  • Regression vs Time Series
  • Examples of Time Series data
  • Trend, Seasonality, Noise and Stationarity
  • Time Series Operations
  • Detrending
  • Successive Differences
  • Moving Average and Smoothing
  • Exponentially weighted forecasting model
  • Lagging
  • Correlation and Auto-correlation
  • Holt Winters Methods
  • Single Exponential smoothing
  • Holt’s linear trend method
  • Holt’s Winter seasonal method
  • Text cleaning, regular expressions, Stemming, Lemmatization
  • Word cloud, Principal Component Analysis, Bigrams & Trigrams
  • Web scrapping, Text summarization, Lex Rank algorithm
  • Latent Dirichlet Allocation (LDA) Technique
  • Word2vec Architecture (Skip Grams vs CBOW)
  • Text classification, Document vectors, Text classification using Doc2vec



  • Yes. You will get two certificates - one for the training and another for your project work


  • Although we believe that skills are enough to get you hired, however, some companies hiring for DATA Scientist profile in the industry will expect following out of you. FRESH GRAD OR A COLLEGE STUDENT A degree in B.Tech/M.Tech (Any Trade), BCA, MCA or B.Sc (Statistics or Mathematics), BA (Maths or Economics or Stats), B.Com. WORKING PROFESSIONAL Professional experience of 1+ years in Python, R, SAS, Business intelligence, Data warehousing, SQL. If your professional experience is not related to data analytics, you can still make a switch to Data scientist provided that you hold any of the degrees specified above.


  • Although it will not likely to happen to see our past success rate. We will try every inch of our efforts to place you. However, in case if we fail to do so, we will refund the fee directly into your bank account within 6 months of your course completion date. No questions asked


  • Data science doesn’t need any  technical or programming experience. We will teach  All  Module from Scratch

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