Are you interested in learning more about data science but don’t know where to start? This course will provide you with the key foundational knowledge any data scientist needs. It will prepare you for your career start in data science or further advanced learning in the field.
In this course, you will learn about the various activities that data scientists do.
Do you want to predict if a bank transaction was fraudulent or not? Do you want to forecast your company sales in the upcoming months, so that enough inventory is prepared? Or do you want to assess what chemical compound has the biggest impact on production of a protein that is used for your company's newly developed drug?
We will show you how to do all of that! We will walk you through multiple types of real-world data science projects. You will learn about selecting suitable methods for solving each of these tasks, how these methods work, and on which use cases they can be applied.
What will you learn?
- Data Science fundamentals - data science and business thinking, an overview of machine learning problems and related solution methods, principles of data modelling – how data should be structured, train/test data split, model selection, and validation.
- Testing hypotheses, for example, if a drug is effective in stopping disease, or if company investment in sustainability has a positive impact on its profits: linear regression, panel data regression – fixed and random effects.
- Solving classification tasks such as classifying bank transactions as fraudulent or not: Logistic regression, Decision Trees, Support Vector Machines methods.
- Solving regression tasks such as predicting train delays: Regression Trees, Support Vector Regressions, Random Forest methods.
- Forecasting time series data such as pollution levels or country's population growth: stationarity concept, ARIMA, and Exponential Smoothing methods.
- Finding unseen patterns in your data, for example creating segments of your customers: Principal Component Analysis, k-means clustering, Hierarchical clustering.
Who is this course for?
- For people interested in data science and for those who want to get into the data science field.
- For people who still remember high school math a bit – terms like correlation or weighted average should sound familiar.
How do you finish the course?
You will attend at least 7/9 lectures.
- Aneta Havlínová
She is currently working as a data scientist (applied economist) in Spaceknow, a company focusing on satellite imagery data analytics. She mainly works on forecasting economic indices such as urban growth. Previously, she worked as a data scientist in MSD, where she used her knowledge to help lab scientists with biological processes modelling to provide oncology marketing teams with insights based on financial data. Before entering the field of statistical modelling, she also worked in data visualization and as a business analyst in Raiffeisenbank. She has a master's degree from the Institute of Economic Studies at Charles University in Prague.
- Justina Ivanauskaite
She is an experienced data scientist with a background in statistics. Her expertise lies primarily in econometric modeling, statistics, and simulation. Currently, she is data science lead of Animal Health Advanced Analytics team, which supports research, new product development, manufacturing, and commercial aspects of animal health in MSD. Justina is interested in creating data science solutions with an emphasis on reusability and reproducibility, which delivers value to the client. Justina is interested in creating data science solutions that bring value to the client, with emphasis on reusability and reproducibility.
- Mário Vozár
He is an analytics leader helping with finding value in data for his company's business partners. Having advanced degrees in Mathematics and Economics allows him to combine technical expertise with understanding business needs. Currently, he is Data Science Director in Marketing Agency Omnicom Media Group. His team supports his clients in areas like automated reporting, Marketing Mix Modelling and Optimization, Forecasting, and others. He enjoys brainstorming with his team on approaching and solving new analytical challenges.
- Pavel Fišer
He is a data scientist within Animal health space with prior experience from various projects in R&D, Manufacturing, and commercial space. He enjoys working on projects to apply new approaches and create new methods to help businesses function more efficiently.
- Andrea Štefancová
She is a graduate from University of Economics in Prague with masters' in Econometrics and Operations Research. She works as a data scientist in MSD Animal Health with prior experience from clinical research, quality assurance, and global operations. Andrea mainly enjoys solving optimization problems. Within her current project, she works on finding optimal safety stocks of animal medicine products in MSD distribution centers. She also has a lot of experience creating web-based applications in R software, allowing business users easy access to statistical models developed by data scientists.
- Kateřina Zelinková
She is a data scientist with a background in applied mathematics and statistics. In her nearly 3 years working in the field, she experienced different areas such as econometrics, simulation, Bayesian analysis, data analytics, cleaning of data, optimization of code, interpretation of modelling and so on. She has collaborated on projects impacting Animal Health division, human health and development, and application of novel method in clinical trials. She has great passion in data science, loves to learn new things and talk with others about the field and looks forward to share her enthusiasm with other nerds and enthusiasts.