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Data Science Foundation

Course Summary

Data Science is becoming the most important element in any industry or business. Many organizations including fortune 500 companies, already aligning their organization structure to facilitate the data science-driven decision making at various levels. From the reports by mainstream think tanks, top consulting companies, it is evident that, beyond any doubts, that either an organization or an individual professional, not gaining the Data Science skill would face uncertainty in near future.

Data Science Foundation is a high-level Data Science course designed with an agenda covering all aspects of data science with core concepts. Data Science has three board domains viz., Statistics, Machine Learning/ Programming/ Data Skills, Business Domain Knowledge. The foundation courses focus on basic statistics, an overview of Machine Learning with hands-on practice with a couple of ML algorithms and application of Data Science in top 5 industry domains.

The Data Science Foundation course provides you with solid foundation for Data Science knowledge. This course is equally valuable for beginners, professionals at various levels. If you don't want to be left behind with this huge Data Science wave, this is the course for you.


Course Objectives

After successful completion of this course, you should have

  • Gained the idea about the bigger picture of Data Science, where and how you/your organisation organization can benefit from Data Science
  • Essential knowledge of Statistics related to Data Science
  • Learned to perform data analysis for a large set of data using various tools such as R, Python, Tableau etc.,
  • Understood what is Machine Learning, Artificial Intelligence and various methods to deploy them for business data
  • If you aspire to be a Data Scientist, you will gain a clear road map to becoming one

Course Outline

What is Data Science?

  • Business Analytics vs Big Data vs Data Science
  • Components of Data science: Venn diagram
  • Understanding Data Science Pipeline
  • Roles and Jobs in Data Science in the Market
  • Data Science Team Structure

Data Science: Areas of Study

  • Business / Domain Knowledge
  • Hacking / Programming Skills
  • Statistics
  • Machine Learning

Data Sources and importing Techniques

  • Reading from existing data from flat files and database
  • APIs
  • Web Scraping
  • Other data gathering methods
  • Hands on importing data

Data Exploratory Analysis

  • Basic statistics
  • Exploratory graphs
  • Exploratory statistics

Data Science Programming

  • Programming basics
  • Python overview
  • R overview
  • SQL

Introduction to Applied Statistics

  • Hypothesis Testing
  • Confidence
  • Validating

Introduction to Machine Learning

  • Decision trees
  • Ensembles
  • k-nearest neighbors (kNN)
  • Naive Bayes classifiers
  • Artificial neural networks

Visual Analytics with Tableau

  • Introduction to Visual Analytics
  • Interpretability
  • Actionable insights
  • Various Graphical Representations

Data Science Career and Conclusion

  • Data Science Learning sources
  • Data Science relevance in your career
  • How should a data scientist’s resume look like
  • Tips on job application

Who can beneit from this course?

This course is not specific for any industry as well as professional experience. Anyone from beginners to business leaders, aspiring to gain Data Science knowledge can pursue this course.

  • Beginners (Graduates or students) from any discipline aspiring to pursue a career in Data Science, analytics, big data domains
  • Working professionals looking to change their domain to the analytics/data science field
  • Professionals, who are already working on data analysis/analytics domain, want to enhance their skills in mainstream Data Science
  • Candidates looking for Data Science knowledge as well as certification as formal testimony of their skills
  • Anyone, aspiring knowledge in the field of Data Science

Course Objectives

After successful completion of this course, you should have

  • Gained the idea about the bigger picture of Data Science, where and how you/your organisation organization can benefit from Data Science
  • Essential knowledge of Statistics related to Data Science
  • Learned to perform data analysis for a large set of data using various tools such as R, Python, Tableau etc.,
  • Understood what is Machine Learning, Artificial Intelligence and various methods to deploy them for business data
  • If you aspire to be a Data Scientist, you will gain a clear road map to becoming one

Course Outline

What is Data Science?

  • Business Analytics vs Big Data vs Data Science
  • Components of Data science: Venn diagram
  • Understanding Data Science Pipeline
  • Roles and Jobs in Data Science in the Market
  • Data Science Team Structure

Data Science: Areas of Study

  • Business / Domain Knowledge
  • Hacking / Programming Skills
  • Statistics
  • Machine Learning

Data Sources and importing Techniques

  • Reading from existing data from flat files and database
  • APIs
  • Web Scraping
  • Other data gathering methods
  • Hands on importing data

Data Exploratory Analysis

  • Basic statistics
  • Exploratory graphs
  • Exploratory statistics

Data Science Programming

  • Programming basics
  • Python overview
  • R overview
  • SQL

Introduction to Applied Statistics

  • Hypothesis Testing
  • Confidence
  • Validating

Introduction to Machine Learning

  • Decision trees
  • Ensembles
  • k-nearest neighbors (kNN)
  • Naive Bayes classifiers
  • Artificial neural networks

Visual Analytics with Tableau

  • Introduction to Visual Analytics
  • Interpretability
  • Actionable insights
  • Various Graphical Representations

Data Science Career and Conclusion

  • Data Science Learning sources
  • Data Science relevance in your career
  • How should a data scientist’s resume look like
  • Tips on job application

Who can beneit from this course?

This course is not specific for any industry as well as professional experience. Anyone from beginners to business leaders, aspiring to gain Data Science knowledge can pursue this course.

  • Beginners (Graduates or students) from any discipline aspiring to pursue a career in Data Science, analytics, big data domains
  • Working professionals looking to change their domain to the analytics/data science field
  • Professionals, who are already working on data analysis/analytics domain, want to enhance their skills in mainstream Data Science
  • Candidates looking for Data Science knowledge as well as certification as formal testimony of their skills
  • Anyone, aspiring knowledge in the field of Data Science

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