Post Graduate Certification in Data Science

Courses

Description

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

Curriculum

Foundations

Introduction to programming using Python(3 weeks)

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

Exploring Data Analysis( 1 Week)

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

Statistical Methods for Decision Making(1 week)

Database Management System using My SQL Workbench(2 weeks)

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

Linear and Logistic Regression

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

Supervised Learning Classification

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

Unsupervised Learning

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.

Ensemble Techniques( 1 week)

Bagging
Boosting
Bagging & Boosting Examples

Data Visualization Using Tableau(2 week)

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

Time Series (1 week)

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
ARIMA and SARIMA

Text Mining ( 1 week)

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

FAQ

Q1. WILL I GET CERTIFICATE ALSO FROM TRAINING
Yes. You will get two certificates - one for the training and another for your project work

Q2. WHAT IS THE ELIGIBILITY FOR PROGRAM?
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.

Q3. HOW DOES REFUND OR MONEY BACK WORK IN CASE I AM NOT PLACED
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

Q4. I DONT HAVE TECHNICAL BACKGROUND OR TECHNICAL KNOWLEDGE
Data science doesn’t need any technical or programming experience. We will teach All Module from Scratch

Public Batches Phone No.: +1-844-889-4054 Email: support@careerera.com
Private Batches Phone No.: +1-844-889-4054 Email: Sandeep@careerera.com
Corporate Batches Phone No.: +1-844-889-4054 Email: aditya@careerera.com

Post Graduate Program in Data Science