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Data Science Crash course for Business
19
Jan
Course Description
Leaders today manage organizations operating in highly complex, dynamic, and globally competitive business environments. In this age of data, the world is becoming more data-driven by the day. Scientific data analysis has become all-pervasive, making it one of the fastest-growing and most profoundly essential fields in management. Managers need to learn how to interpret their data to make decisive decisions. Operational data must maximize your impact while reducing the work required to achieve strategic objectives. The ability to use internal and external data is a skill that must help us to solve business problems and avoid the insanity of repeating the same action while expecting different results. Only organization leaders with a razor-sharp capability to consume and interpret data-rich environments and precisely translate that into strategic, operational decisions will successfully command future industry leadership and competitive dominance. Our world-class offering is designed for business professionals seeking to be agents of transformative change within their organizations.
Course Objectives
Understanding how data science techniques can be applied to influence successful strategic decision-making is crucial for every analyst in any modern-day organization. This training course is essential for professionals with an interest in data science and could be well-suited for; Operations, accountants, HR, Management, and all business professionals
Learning Outcomes
At the end of this course, learners will gain the ability to:
- Learn the modern way to extract data from relational databases.
- Learn modern ways to explore, prepare and assess the quality of the data using Python and present data using Tableau.
- Learn how to use basic to advanced analytics techniques for business forecasting, recommendation, and customer segmentation.
- Quickly and easily use actionable insights to improve decision-making.
Toolkit
SQL, Tableau, Python/R, Postgres
Training Methodology
This training course will combine instructor-led presentations with interactive discussions between participating delegates and their interests. It is presented very hands-on to suit individuals with varying levels of knowledge and experience. In addition, practical exercises, video material, and case studies will stimulate and support these discussions to maximize the number of participants. Above all, the course facilitators will extensively use case examples and case studies based on real-life strategic issues and situations in which he has been personally involved
Course Schedule
- Week 1: Data extraction & data inventory assessment using SQL.
- Week 2: Exploratory data analysis using Tableau & Take-home project
- Week 3: Introduction to data analytics models using Python & Take-home project
- Week 4: Machine learning (Predictive, associative & segmentation models) & Capstone project
Course Duration, Location & Investment
At the end of this course, learners will gain the ability to:
- Duration: 4 Weeks (4 Hours per week).
- Venue: Remote (Evening Classes & Weekends)
- Investment: Ksh 23,000 per head / Ksh 40,000 for groups of 2
Course Content
Total learning: 19 lessons
Time: 10 weeks
-
Week 1: Data extraction & data inventory assessment using SQL.
- SQL basics: Basic SQL syntax, keywords and clauses.
- Data extraction: Understanding how to extract data from a database using SQL queries.
- Data inventory assessment: Understanding how to assess the data inventory in a database to analyze data and identify patterns.
- Advanced SQL topics: You will learn advanced SQL concepts such as subqueries, stored procedures, and views.
- Database optimization techniques.
-
Week 2: Exploratory data analysis using python & Take-home project
- Python basics: Introduction to python for Data Science and common libraries such as numpy, pandas, etc.
- Introduction to descriptive statistics in python.
- Exploratory Data Analysis (EDA) and data visualization
- Inferential statistics using Python.
- Data storytelling: This includes understanding how to use skills learned from previous sessions above to tell a story and communicate insights to a non-technical audience.
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Week 3: Predictive analytics and Machine learning.
- Introduction to Data Science: This section will cover the basics of data science and its role in today’s business world. Topics will include the data science process, data types, and common applications.
- Data Exploration and Visualization: This section will focus on understanding and exploring data using various tools and techniques. Topics will include data cleaning, data visualization, and basic data exploration methods.
- Predictive Modeling: This section will cover the basics of predictive modeling, including linear and logistic regression, decision trees, and random forests. Topics will also include model evaluation and selection, Feature engineering, Multicollinearity Checks, and Feature Selection.
- Model optimization techniques e.g. Hyper-parameter tuning.
- Thresholding.
-
Week 4: Predictive analytics and Machine learning Cont....
- Data Mining: This section will cover data mining techniques, including clustering, association rule mining, and text mining. Topics will also include natural language processing and sentiment analysis.
- Big Data and Machine Learning: This section will cover big data technologies, including machine learning algorithms, including supervised and unsupervised learning.
- Data Science in Practice: This section will cover the practical applications of data science, including case studies and real-world examples. Topics will include how to communicate data science findings and how to implement data science solutions in a business setting.
- Capstone Project.
Target audiences
- Operations, accountant, HR, Management and all business professionals
Instructor
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