Data Analytics & Data Science for Executives

Name: Data Analytics & Data Science for Executives
Rating: 4.6 (693 reviews)

Unlock the power of data to drive informed business decisions with our Data Analytics for Executives Course

Created byDMSA Training

Last updated 2/2023

English

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What you'll learn

What Data Analytics is and why it is one of the most in-demand skills today
The history of Data Analytics in helping companies become market leaders
Why ethics is key for trusted analytics
How to scale analytics across the organization
The core principles of data analytics, from exploratory data analysis and data visualization to high-frequency analytics and alternative data

Course content

8 sections • 23 lectures • 2h 4m total length

Introduction to Data Analytics10:25
Introduce data analytics, what data is, and how insights guide decisions. Cover data types, data monetization, data engineering, and AI ethics shaping practical analytics.
Introduction to Data Analytics
What is Data Analytics8:34
Explore how data analytics drives revenue and reduces costs by elevating data strategy at the executive level, and trace the data science maturity curve from descriptive to prescriptive analytics.
What is Data Analytics
History of Data Science7:34
Trace the evolution of data science from postwar research to modern data analytics, highlighting corporate experimentation, data as an asset, and the Capital One story.
History of Data Science
What is Data Science6:32
Define data science as practice of uncovering insights in big data, balancing data-driven decisions with domain expertise, and clarifying artificial intelligence, machine learning, deep learning, and supervised versus unsupervised learning.
What is Data Science

How to Perform Data Science7:49
Explore how data science clusters data, uses regression and classification, and forecasts. Learn about data quality, cleaning, model deployment, visualization, and drift monitoring, recognizing data as an asset.
How to Perform Data Science
Data Team5:06
Explore the roles in a data science team, from data scientists building models to data engineers designing pipelines, and learn how analytics translators bridge business and analytics.
Data Team
Data Visualization9:24
Master data visualization fundamentals, from Excel charts to D3 and Python libraries such as seaborn, while embracing ethical, accessible storytelling that clearly communicates insights to diverse audiences.
Data Visualization
Data Analytics in Excel Podcast0:12

Data Across The Spectrum6:32
Discover how defensive and offensive analytics shape data management and analytics, prioritize privacy and data quality, and enable predictive and prescriptive insights across fraud prevention and growth opportunities.
Data Across the Spectrum
Data Monetization2:39
Explore the economics of data monetization and how organizations capture value through internal cost savings, better decisions, and external methods like selling data, APIs, and market reports.
Data Monetization
Data & Concept Drift6:27
Explore how data and concept drift affect machine learning models as data and environments evolve, and learn to monitor, retrain, and decommission models to maintain performance.
Data & Concept Drift
Financial Modelling Meets Data Analytics0:14

High Frequency Data5:17
Explore high frequency data and real-time insights for now costing and forecasting. See dashboards that combine spend data with employment and vaccinations trends to reveal grocery and entertainment trends.
High Frequency Data Analytics - Background5:20
High Frequency Data
Alternative Data7:18
Explore how alternative data broadens decision making beyond finance, using geolocation, satellite imagery, traffic, and credit card insights. Assess its timeliness and edge against costs, data quality, and vendor reliance.
Alternative Data
Data Analytics in Finance1:29

AI Ethics14:53
It’s one thing having data governance, but we need governance across the whole data value chain – from data capturing to data use and presentation. I won’t delve too much into the ethics of data presentation and visualisation. I will instead point you in the direction of some fantastic books on the topic, such as How Charts Lie: Getting Smarter about Visual Information by Alberto Cairo. It was one of my top Data Analytics Book Recommendations.
Data Governance vs. Model Governance and AI Ethics3:05
AI Ethics

Data Analytics Books Recommendations2:28
If there is one aspect of data analytics that I’ve realised in exceptionally important, it is the ability to tell a story with data.
More Data Analytics Books Recommendations2:32
These books are my go-to guides for beginner to advanced data heads (to steal a phrase from Jordan Goldmeier).
Podcast Recommendations1:19
Podcast recommendations to learn more while listening.

Requirements

Basic numeracy and business knowledge will be valuable, but not essential for this course.

Description

A Data Science Training course for Executives is a course designed to provide business leaders with an understanding of the fundamental concepts and applications of data science. The course covers key topics such as data analysis, data visualization, machine learning, and big data technologies. The goal of the course is to equip executives with the skills and knowledge they need to make informed decisions about data-driven initiatives within their organizations.

Data Analytics is one of the most sought-after skills globally. Business Insider has even rated both Chief Digital Officers and Data Science Executives as two of the top six most in-demand executive jobs.

However, analytics knowledge isn’t just crucial for data scientists and analysts, but for anyone looking to navigate today’s world, make data-informed decisions and be able to predict and better react to market trends.

This Data Science for Executives course explains:

1. What Data Analytics is and why it is one of the most in-demand skills today.

2. The history of Data Analytics in helping companies become market leaders and how companies came to recognise data as an asset rather than a by-product of operations.

3. Why ethics is key for trusted analytics both for the organisation and for clients.

4. How to scale analytics across the organisation using data scientists and data engineers.

5. The core principles of data analytics, from exploratory data analysis and data visualisation to high-frequency analytics and alternative data.

This course covers analytics at a high level without any coding examples, by focusing on the various aspects of analytics and how they can be applied in practice. This course will enable executives and beginners alike to understand the power of analytics, what skills data science teams need, and why analytics needs to be broadly adopted to enable a data-driven organisation.

This course was developed by DMSA and is presented by Data Science leader Matthew Bernath.

Who this course is for:

Today’s largest organisations run on data, in fact some of them have data as their biggest asset. Despite this incredible volume of data, many businesses are not well placed to extract value from this data as they do not have the right skills. This course teaches everyone in an organisation what data is, how to extract value from it and how the history of data science in business has shown us that data is worth investing in.

Data Analytics & Data Science for Executives

What you'll learn

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Course content

Introduction4 lectures • 33min

The Four Types of Data Analytics1 lecture • 6min

Data Science Work and the Data Science Team4 lectures • 23min

The Value of Data - Monetising Data Science and Finding its Role4 lectures • 16min

High Frequency and Alternative Data4 lectures • 19min

Data Science Zen - Data Science for Purpose1 lecture • 3min

Ethical Data Science2 lectures • 18min

Recommended Reading & Listening3 lectures • 6min

Requirements

Description

Who this course is for: