Datascience

Who Should Do The Data Science Course?

Professionals who can consider Data Science course as a next logical move to enhance in their careers include:

• Professional from any domain who has logical, mathematical and analytical skills
• Professionals working on Business intelligence, Data Warehousing and reporting tools
• Statisticians, Economists, Mathematicians
• Software programmers
• Business analysts
• Six Sigma consultants
• Fresher from any stream with good Analytical and logical skills

• Module 1 – SQL And RDBMS
• Module 2 – R & R Studio
• Module 3 – Introduction To Python
• Module 4 – Basic Python
• Module 5- Working With Libraries Like NumPy, Pandas, Matplotlib, Seaborn, SciPy, Sklearn In Python
• Module 6 – Working Experience With Pandas In Python
• Module 7 – Working Experience With Matplotlib Library In Python
• Module 8 – To Work With Seaborn Library (High-Level Interface For Drawing Attractive And Informative Statistical Graphics) In Python
• Module 9 – Introduction To SciPy And Sklearn Libraries In Python
• Module 10 – Statistical Analysis
• Module 11 – Hypothesis Testing
• Module 12 – Linear Regression
• Module 13 – Logistic Regression
• Module 14 – Discrete Probability Distribution
• Module 15 – Advanced Regression
• Module 16 – Multinomial Regression
• Module 17 – Data Mining Unsupervised – Clustering
• Module 18 – Dimension Reduction
• Module 19 – Data Mining Unsupervised – Network Analytics
• Module 20 – Data Mining Unsupervised – Association Rules
• Module 21 – Data Mining Unsupervised – Recommender System
• Module 22 – Machine Learning Classifiers – KNN
• Module 23 – An Introduction To Data Visualization
• Module 24 – Tableau Products And Usage
• Module 25 – Basic Charts On Tableau
• Module 26 – Connecting Tableau With Multiple Sheets And Data Sources
• Module 27 – Tableau Filters And Visualization Interactivity
• Module 28 – Interaction And Grouping The Data
• Module 29 – Time Series Chart
• Module 30 – Maps And Images In Tableau
• Module 31 – Advanced Charts In Tableau And Analytical Techniques
• Module 32 – Calculations On Tableau
• Module 33 – Tableau Integration With Other Tools
• Module 34 – Understand The Business Problem
• Module 35 – Data Collection
• Module 36 – Data Cleansing / Exploratory Data Analysis / Feature Engineering
• Module 37 – Data Mining
• Module 38 – Model Deployment
• Module 39 – Introduction To Big Data
• Module 40 – Hadoop And Its Components
• Module 41 – Linux OS And Virtualization Softwares
• Module 42 – Apache Hive
• Module 43 – Apache SQOOP
• Module 44 – Apache Spark
• Module 45 – Azure
• Module 46 – Classifier – Naive Bayes
• Module 47 – Bagging And Boosting
• Module 48 – Decision Tree And Random Forest
• Module 49 – Black Box Methods
• Module 50 – Text Mining
• Module 51 – Natural Language Processing
• Module 52 – Forecasting
• Module 53 – Assignments
• Module 54 – Projects

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