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Data Science

Learn in-demand skills with widely recognized and valued Post Graduate Diploma in Data Science by EduHac

300+

Students Empowered

Online

Formate

11 Months

Recommended 10-12 hrs/week

July  26, 2019

Start Date

130+

Hiring Partners

Program Overview

Key Highlights

  • Designed for Working Professionals
  • 7+ Case Studies and Projects
  • One-on-One with Industry Mentors
  • Dedicated Student Success Manager
  • Job Placement Assistance with Top Firms
  • 400+ Hours of Learning
  • Practical Hands-on Workshops
  • Timely Doubt Resolution
  • No Cost EMI Option

EduHac has been instrumental in helping us in finding candidates with key skill sets in Data Science and Analytics

– Val S, Director at Animaker

Web-flow-Logo@Eduhac

Top Skills You Will Learn

Statistics, Predictive Analytics using Python, Machine Learning, Data Visualization, Big Data Analytics, etc.

Job Opportunities

Data Analyst, Data Scientist, Product Analyst, Machine Learning Engineer, Business Analyst

Who Is This Program For?

Engineers, Software and IT Professionals, Marketing and Sales Professionals, Managers

Minimum Eligibility

Bachelor’s degree, no coding experience required

Programming Languages and Tools Covered

Datascience-With-Python-Eduhac
Datascience-With-Tableau-Eduhac
Datascience-With-Spark-Eduhac
Datascience-With-Hadoop-Eduhac
Datascience-With-MySQL-Eduhac
Datascience-With-Hive@Eduhac

Programming Languages and Tools Covered

Syllabus

‣ Introduction To DataScience
‣ Real Time UseCases Of DataScience
‣ Who is a DataScientist??
‣ Github Tutorial
‣ Skillsets needed for DataScientist
‣ 6 Steps to take in 3 Months for a complete transformation to DataScience from any other domain
‣ Machine Learning-Giving Computers The ability to learn from data
‣ Supervised vs Unsupervised
‣ DeepLearning vs Machine Learning
‣ Link to get Free Data to Practice?
‣ Some Great self Learning DataScience Resources(Books, Tutorials, Videos, Papers)

Python Fundamentals begins with acquiring an in-depth knowledge of the Python programming language. By the end of the week, students will be expected to program intermediate level scripts in Python.

‣ Software Installation
‣ Introduction To Python
‣ “Hello Python Program” in IDLE
‣ Jupyter Notebook Tutorial
‣ Spyder Tutorial
‣ Introduction to Python
‣ Variable,Operators,DataTypes
‣ If Else, For and While Loops
‣ Functions
‣ Lambda Expression
‣ Filter, Map, Reduce
‣ Taking input from the keyboard
‣ HANDS ON-
‣ INTERVIEW QUESTION DISCUSSION

NumPy

‣ Create Arrays
‣ Array Item Selection and Indexing
‣ Array Mathematics
‣ Array Operation
‣ HANDS ON

Pandas

‣ Introduction to Pandas
‣ Series
‣ Series indexing and Selection
‣ Series Operation
‣ Introduction to Pandas
‣ Data Frames
‣ Data Collection from CSV, JSON, HTML,excel
‣ Data Merging,Concatenation, join
‣ Group By and Aggregate Function
‣ Order By
‣ Missing Value Treatment
‣ Outlier Detection and Removal
‣ Pandas builtin Data Visualisation
‣ HANDS ON
‣ INTERVIEW QUESTION DISCUSSION

Visualisation (matplotlib & seaborn)

we’ll begin curriculum focused on various data visualization techniques and how they can help us engage and learn from our data using Matplotlib, Seaborn, ggplot

‣ Line Plots
‣ Scatter Plots
‣ Pair Plots
‣ Histograms
‣ Heat Maps
‣ Bar Plots
‣ Count Plots
‣ Factor Plots
‣ Box Plots
‣ Violin Plots
‣ Swarm Plots
‣ Strip Plots
‣ Pandas Builtin Visualisation Library
‣ HANDS ON
‣ INTERVIEW QUESTION DISCUSSION

This session is dedicated to creating a deep understanding of mathematical concepts we’ll later see in topics like machine learning and statistical analysis. Contrary to the traditional mathematics course, students will learn statistics and linear algebra in a programmatic way to fit a problem’s needs.

‣ Descriptive vs Inferential Statistics
‣ Mean, Median, Mode, Variance,Std. dev
‣ Central Limit Theorm
‣ Co-Variance
‣ Pearson’s Product Moment Correlation
‣ R – Square
‣ Adjusted R-Square
‣ Spearman’s. Rank order Coefficient
‣ Sample vs Population
‣ Standardizing Data(Z-score)
‣ Hypothesis Testing
‣ Normal Distribution
‣ Bias-Variance Tradeoff
‣ Skewness
‣ P Value
‣ Z-test vs T-test
‣ The F distribution
‣ The chi-Square test of Independence
‣ Type-1 and Type-2 errors
‣ Annova
‣ HANDS ON
‣ INTERVIEW QUESTION DISCUSSION

‣ Introduction to Machine Learning
‣ Machine Learning Usecases
‣ Supervised vs Unsupervised vs Semi-Supervised
‣ Machine Learning process Workflow
‣ Training a model
‣ Validating results
‣ Overfitting vs Underfitting
‣ Ordinal vs Nominal data
‣ Structured vs unstructured vs semistructured data
‣ Intro to scikitLearn
‣ HANDS ON

‣ Regression Vs Classification
‣ Linear regression
‣ Multivariate regression
‣ Polynomial regression
‣ Multi-Colinearity,
‣ Auto correlation
‣ Heteroscedascity
‣ Hands On

‣ KNN
‣ Svm
‣ Decision Tree
‣ Random Forest
‣ Performance tuning of Random Forest
‣ Naive Bayse
‣ Overfitting Vs Underfitting
‣ Hands-On

‣ Classification Report
‣ Confusion Report
‣ ROC
‣ RMSE
‣ MSE
‣ Cross validation
‣ Hands On

‣ Kmeans
‣ How to choose the number of K in KMeans
‣ Hands-on
‣ PCA
‣ Hands-on

‣ What is Ensembling
‣ Types of Ensembling
‣ Bagging
‣ Boosting
‣ Stacking
‣ Random Forest
‣ Important Feature Extraction
‣ XGBoost
‣ HANDS ON

‣ Tokenizer
‣ Stop Word Removal
‣ Tf-idf
‣ Document similarity
‣ Word2vec Model
‣ t-SNE visualisation
‣ Sentiment Analysis
‣ HANDS ON

‣ Basic of Neural Network
‣ Type of NN
‣ Cost Function
‣ Tensorflow Basics
‣ Hands on Simple NN with Tensorflow
‣ Image classification using CNN
‣ HANDS ON

Industry Projects

Learn through real-life industry projects sponsored by top companies across industries

  • Engage in collaborative projects with student-mentor interaction
  • Benefit by learning in-person with expert mentors
  • Personalised subjective feedback on your submissions to facilitate improvement

Industry Projects

Learn through real-life industry projects sponsored by top companies across industries

  • Engage in collaborative projects with student-mentor interaction
  • Benefit by learning in-person with expert mentors
  • Personalised subjective feedback on your submissions to facilitate improvement

The upGrad AdvantageIndustry Projects

Learning-Support@Eduhac
Industry Mentors
  • Receive unparalleled guidance from industry mentors, teaching assistants and graders
  • Receive one-on-one feedback on submissions and personalised feedbacks on improvement
Student Success Managers
  • A dedicated Success Managers is allocated to each student so as to ensure consistent progress
  • Success Managers are your single points of contact for all your non-academic queries
Discussion@Eduhac
Q&A Forum
  • Timely doubt resolution by Industry experts and peers
  • 100% Expert-verified responses to ensure quality learning
Expert Feedback
  • Personalised expert feedback on assignments and projects
  • Regular live sessions by experts to clarify concept related doubts
Networking@Eduhac
EduHac basecamp
  • Fun-packed, informative and career building workshops Sessions by industry professionals and professors
  • Group activities with your peers and alumni
Industry Networking
  • Live sessions by experts on various industry topics
  • One-on-one discussion and feedback sessions with industry mentors