Data Science Training Institute

Data science Training

Course:- Data Science

Enrolled Students:- 15+

Duration:- 2 months

Level:- Basic to Advance

About The Course

To discover the hidden actionable insights in an organization’s data, data scientists mix math and statistics, specialised programming, sophisticated analytics, artificial intelligence (AI), and machine learning with specialised subject matter expertise. Strategic planning and decision-making can be guided by these findings.

Course Contents

  • What is data science?
  • How is data science different from BI and Reporting?
  • What is difference between AI, Data Science, Machine Learning, Deep Learning
  • Job Land scape and Preparation Time
  • Who are data scientists?
  • What skillsets are required?
  • What is day to day job of Data Scientist
  • What kind of projects they work on?
  • End to End Data Science Project Life Cycle
  • Data Science roles – functions, pay across domains, experience
  • Data types
  • Continuous variables
  • Ordinal Variables
  • Categorical variables
  • Time Series
  • Miscellaneous
  • Common Data Science Terminology
  • Descriptive statistics
  • Basics concepts of probability
  • Frequentist versus Bayesian Probability
  • Axioms of probability theory,
  • Permutations and combinations
  • A Primer to R programming
  • What is R? Similarities to OOP and SQL
  • Types of objects in R – lists, matrices, arrays, data.frames etc
  • Creating new variables or updating existing variables
  • If statements and conditional loops – For, while etc.
  • String manipulations
  • Understanding the reason of Python’s popularity
  • Basics of Python: Operations, loops, functions, dictionaries
  • Numpy – creating arrays, reading, writing, manipulation techniques
  • Ground-up for Deep-Learning
  • Getting to understand structure of Matplotlib
  • Configuring grid, ticks.
  • text, color map, markers, widths with Matplotlib
  • configuring axes, grid,
  • hist, scatterplots
  • bar charts
  • multiple plots
  • 3D plots
  • Introduction to pandas
  • Data loading with Pandas
  • Data types with python
  • Descriptive Statistics with Pandas
  • Quartile analysis with Pandas
  • Sort, Merge, join with Pandas
  • Indexing and Slicing with pandas
  • Dealing Prediction problem
  • Forecasting for industry
  • Optimization in logistics
  • Segmentation in customer analytics
  • Supervised learning
  • Linear Regression
  • Assumptions
  • Model development and interpretation
  • Sum of least squares
  • Supervised Learning
  • Decision trees and Random Forest
  • Classification and Regression trees(CART)
  • Process of tree building
  • Entropy and Gini Index
  • Problem of over fitting
  • Pruning a tree back
  • Trees for Prediction (Linear) – example
  • NLP I – Text Preprocessing
  • Tokenization
  • Stemming
  • Lemmatization
  • NLP II – Text Modelling
  • ReLU
  • Sigmoid, Depth vs Width tradeoffs
  • Convolutional networks
  • Concepts of filters
  • Sliding
  • Business problem to an analytical problem
  • Guidelines in model development
  • Big data and analytics?
  • Leverage Big data platforms for Data Science
  • Introduction to evolving tools
  • Machine learning with Spark
  • Why is it important for Data-Analyst
  • Tableau workbook walkthrough
  • Instruction of creation of your own workbooks
  • Demo of few more workbooks

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