Our Programs - Data Science

Data Science

As the plethora of connected devices continues to rise and the Internet of Things (IoT) no longer remains a novelty, the amount of data we generate continues to rise exponentially as well. In fact, data will nearly double in size every two years if current trends continue. It’s no secret that jobs in big data and advanced analytics are in high demand. At StackRoute Learning, our Data Science programs are carefully designed to meet the demands for this booming profession.

37%

Year-over-year growth for data scientists

Source: LinkedIn’s 2020 Emerging Jobs Report

38%

Year-over-year growth for data engineers

Source: LinkedIn’s 2020 Emerging Jobs Report

74%

Year-over-year growth for specialist AI engineers

Source: LinkedIn’s 2020 Emerging Jobs Report

Professional Certificate in Python Programming and Big Data Analytics

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Time to Complete:

Full time option – 12 weeks | Part-time option – 20 weeks (4 hrs per day x 6 days each week)

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Pre-requisite:

Associates Degree (or higher) from an accredited institution

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Program Type:

PROFESSIONAL CERTIFICATION Powered by StackRoute

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Program Level:

Foundational + Intermediate


What Students Will Learn

Statistics for Data Science
  • Descriptive Statistics

  • Statistical techniques for forecasting and variance analysis

Programming in Python

  • Learn and apply structured programming techniques

  • Understand and implement object-oriented concepts using Python

  • Write SQL queries to retrieve, manage, and manipulate data from RDBMSs (MySQL)

  • Utilize appropriate data structures and algorithms to solve problems

  • Working with Numpy & Pandas libraries

  • Complete a non-trivial Python project on statistical analysis of data sets to demonstrate all the skills acquired

Data Engineering

  • Source, Collect, Clean, prepare and store data in a query friendly form

  • Work with different types of database (NoSQL, Graph, Columnar, time series data)

  • Select, query and aggregate data

Data Analysis, Visualization and Business Intelligence

  • Create data visualization using Python and Tableau

  • Build dash boards & Publish

Software Engineering

  • Use Git to manage code repository, code versioning and configuration management

  • Work in an Agile team environment

  • Apply software engineering and clean coding practices

Professional Skills

  • Develop strong sense of self-efficacy that enables learner to take up challenges confidently

  • Sharpen communication and articulation capabilities

  • Collaborate with others to do Data Analytics

  • Use effective story telling techniques using data

Potential Job Opportunities

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Python Programmer

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Analyst / Data Analyst

Professional Certificate in Data Science

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Time to Complete:

Full time option – 18 weeks | Part-time option – 30 weeks (4 hrs per day x 6 days each week)

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Pre-requisite:

Bachelor Degree (or higher) from an accredited institution

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Program Type:

PROFESSIONAL CERTIFICATION Powered by StackRoute

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Program Level:

Advanced


What Students Will Learn

Statistics for Data Science
  • Descriptive Statistics

  • Statistical techniques for forecasting and variance analysis

Programming in Python

  • Learn and apply structured programming techniques

  • Understand and implement object-oriented concepts using Python

  • Write SQL queries to retrieve, manage, and manipulate data from RDBMSs (MySQL)

  • Utilize appropriate data structures and algorithms to solve problems

  • Working with Numpy & Pandas libraries

  • Complete a non-trivial Python project on statistical analysis of data sets to demonstrate all the skills acquired

Data Engineering

  • Source, Collect, Clean, prepare and store data in a query friendly form

  • Work with different types of database (NoSQL, Graph, Columnar, time series data)

  • Select, query and aggregate data

Data Analysis, Visualization and Business Intelligence

  • Create data visualization using Python and Tableau

  • Build dash boards & Publish

ML in Data Science

  • Apply appropriate modelling techniques - classification, regression and clustering

  • Use supervised and unsupervised learning

  • Fundamentals of Deep Learning

  • Use tools such as Prometheus & Graffana to monitor ML models

  • Use NLTK for text analytics

  • Understand ML Ops for deploying ML models on the Cloud

Software Engineering

  • Use Git to manage code repository, code versioning and configuration management

  • Work in an Agile team environment

  • Apply software engineering and clean coding practices

  • Exposure to CRISP-DM framework

Professional Skills

  • Develop strong sense of self-efficacy that enables learner to take up challenges confidently

  • Sharpen communication and articulation capabilities

  • Collaborate with others to do Data Analytics

  • Use effective story telling techniques using data

Potential Job Opportunities

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Analyst / Data Analyst

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Data Science Professional

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Jr. Data Scientist

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