Introduction:

There is a serious shortage of Data Scientists, and this is a major concern for Top MNCs around the world. All this means the major corporations are ready to pay top dollar salaries for professionals with the right Data Science skills. This Data Science Course equips with all the latest technologies in Big Data, analytics, and R programming. 
Thus, you can easily take your career to the next level after completion of this Data Science Course. 

  • Data Scientist is the best job of the 21st century – Harvard Business Review 
  • Global Big Data market to reach $122B in revenue by 2025 – Frost & Sullivan
  • The US alone could face a shortage of 1.4 -1.9 million Big Data Analysts by 2018 – Mckinsey

Objectives:

  • Understand the Fundamentals of Data Science
  • Learn key concepts such as data manipulation, cleaning, and exploration.
  • Develop Proficiency in Data Science Tools and Programming
  • Gain hands-on experience with tools like Python, R, SQL, and Jupyter Notebooks.
  • Master Data Visualization Techniques
  • Use libraries such as Matplotlib, Seaborn, and Tableau to communicate insights visually.
  • Apply Machine Learning Algorithms and Models
  • Build and evaluate predictive models using supervised and unsupervised learning techniques.
  • Learn Data Wrangling and Preprocessing Skills
  • Handle missing data, outliers, and transform datasets for accurate analysis.
  • Implement Big Data and Cloud-based Solutions
  • Work with large datasets and explore platforms like Hadoop, Spark, and AWS.
  • Develop Real-world Problem-Solving Skills
  • Engage in hands-on projects and case studies to solve practical business problems using data analytics.

Course Outline:

Introduction to Data Science and Statistical Analytics:

  • Introduction to Data Science 
  • Use cases 
  • The need for Business Analytics 
  • Data Science Life Cycle 
  • Different tools available for Data Science

Introduction to R:

  • Installing R and R-Studio 
  • R packages and R Operators 
  • if statements and loops (for, while, repeat, break, next), switch case

Data Exploration, Data Wrangling, and R Data Structure:

  • Importing and Exporting data from an external source 
  • Data exploratory analysis 
  • R Data Structure (Vector, Scalar, Matrices, Array, Data frame, List) 
  • Functions, Apply Functions
  • Bar Graph (Simple, Grouped, Stacked)  
  • Histogram 
  • Pie Chart, Line Chart, Box (Whisker) Plot, Scatter Plot 
  • Correlogram

Introduction to Statistics:

  • Terminologies of Statistics 
  • Measures of Centers, Measures of Spread 
  • Probability 
  • Normal Distribution 
  • Binary Distribution 
  • Hypothesis Testing 
  • Chi-Square Test 
  • ANOVA

Time Series:

  • What is Time Series data? 
  • Time Series variables 
  • Different components of Time Series data 
  • Visualize the data to identify Time Series Components 
  • Implement ARIMA model for forecasting 
  • Exponential smoothing models 
  • Identifying different time series scenario based on which different Exponential Smoothing model can be applied 
  • Implement respective ETS model for forecasting