This foundational course provides a high-level overview of essential Data Science areas. A basic understanding of Data Science from business and technology perspectives is provided, along with an overview of common benefits, challenges, and adoption issues.In this course, you will learn the foundations for Data Science and also learn to use Python - a powerful open source tool. You will come across interesting concepts like exploratory data analysis, statistics fundamentals, hypothesis testing, regression & classification modeling techniques and get introduced to machine learning. The course end project and interview prep will make you industry ready.

Named the sexiest career of the 21st century by none other than the Harvard Business Review, the demand for Data Scientists is rapidly increasing year-on-year. It has been proven that data scientists earn base salaries up to 36% higher than other predictive analytics professionals. Glassdoor reports that the national average salary for a Data Scientist is $1,39,840 in the United States.

KnowledgeHut’s Data Science Foundation course will helps freshers and seasoned professionals alike to gain a deep understanding of the subject and advance your career.

Learning Outcome

  • Data Science Tools & Technologies
    • Get acquainted with various analysis and visualization tools such as matplotlib and seaborn
  • Statistics for Data Science
    • Understand the behavior of data as you build significant models and gain strong concepts on Statistics Fundamentals
  • Python for Data Science
    • Learn about the various libraries offered by Python to manipulate data. Use of various Python libraries like Numpy, Pandas, Scikit-Learn, Statsmodel
  • Exploratory Data Analysis
    • Use Python libraries and work on data manipulation, data preparation and data explorations
  • Data Visualization using Python
    • Use of Python graphics libraries like Matplotlib, Seaborn etc.
  • Advanced Statistics & Predictive Modeling
    • Learn about Analysis of Variance and its practical uses
    • Learn to apply Linear Regression with OLS Estimate to predict a continuous variable
    • Learn to apply Binomial Logistic Regression for Binomial Classification Problems
    • Optimize Model Performance
    • Enhance the model performance using techniques like Feature Engineering and Regularization
    • Dimensionality Reduction Technique
    • Learn the techniques to find the optimum number of components/factors using scree plot, one-eigenvalue criterion
  • Get introduced to Machine Learning - Get to know the basics of machine learning techniques; types of learning and learn about scikit learn library

Who should Attend?

This course is apt for those who are working or wish to work as data scientists. More specifically, this course will suit you if:
  • You are interested in the field of data science and want to learn essential data science skills
  • You are new to Python or are self-taught and you are looking for a more robust, structured learning program
  • You're a Data Analyst, Economist, or Researcher who works with large datasets and wants to make analysis easier and more effective with Python
  • You're a Software or Data Engineer interested in learning the fundamentals of quantitative analysis

Eligibility Criteria

Participants are expected to have:
  • Elementary programming knowledge  
  • Familiarity with statistics
This course is endorsed under Critical Infocomm Technology Resource Programme Plus (CITREP+) Programme.
To find out more about CITREP+ Funding, please refer to Programme Support under CITREP+ page

Information is accurate as of 31 July 2020