DEVrepublik

Data Science for non-technical

DURATION

15 hours

HOW OFTEN

Monday to Friday

START

August

FEE:

3000 UAH

Data Science for non-technical

Artificial intelligence and Machine Learning have the power to transform entire industries. Companies in consumer-facing industries like banking, healthcare, and e-commerce that aren’t using these new tools and advances today run the real risk of failure as we gear towards an automated future.

We understand that time is important that is why this course is short and intense so that you can have all the necessary information in short period of time.

Top skills you will learn in 3 working days (15 hours):

Fee: 3000 uah

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Thu 13

Data Science for non-technical Registration

13.08-18.08

13 August 16:00 - 19:00
Thu 27

Data Science for non-technical Registration

27.08-01.09

27 August 16:00 - 19:00
Thu 13

Data Science for non-technical Registration

13.08-18.08

13 August 16:00 - 19:00
  • about the importance to adopt the Machine Learning and Data Science;
  • how you can use it to strengthen your organization;
  • the skills you need in order to better understand and manage a data team to meet your organization’s needs;
  • the meaning of clustering, computer vision, natural language processing (NLP), deep learning;
  • how to perform a basic machine learning experiment, to understand what machine learning is and how to interpret its output;
  • ways to analyze and visualize your data using dashboards;
  • how to use mathematical tools for data interpretation, understand a statistical significance and how to perform A/B tests;
  • understanding of data sources your company can use, how to store that data and how to write a basic SQL queries in order to pull data from database.

Who is this course for:

This course is an excellent resource for managers and business leaders who are looking for the opportunity to use data science in business but still don’t have much experience in data analytics themselves or just want to strengthen their skills.

 

It is specially designed for executives, for product owners, for project managers, and everyone who need to set proper tasks for their data teams and to read properly their results.

 

Also, it would be a great entry point for the people who are going to start their career in data science.

Curriculum:

1.1. What is Data Science:

  • Difference between Data Analyst, Data Scientist, Data Engineer jobs
  • Data science project workflow
  • Skills, tools, frameworks

1.2. What is Machine Learning:

  • Typical problems that can be solved with ML approaches
  • Difference between Supervised and Unsupervised learning
  • How to estimate the “goodness” of ML model
  • Computer Vision, NLP, Speech Recognition, …
  • ML models deployment

1.3. Data science management:

  • Key positions, roles, team structure
  • Data scientist recruitment
  • Why is it hard to estimate time on a DS (ML/DL) project and why is standard SCRUM not working?

2.1. Data visualization:

  • Types of charts and how to read them
  • Business Inteligence: Power BI

2.2. Data analytics:

  • Basic statistics recap
  • Approximation Results and Confidence Intervals
  • Signicance testing, A/B tests, Simpson paradox

3. Data Collection and Storage:

  • Why data is so valuable and expensive?
  • Big Data
  • Google Analytics
  • Databases, MySQL, Google Big Query, etc.
  • SQL: basic queries + mysql client
  • Google SpreadSheets on steroids: direct import data from DB, management from Python, bussiness processes automation