Data Science and analytics: tools
for humanities and managers
Teach modern methods of data analysis and search for hidden patterns in socio-economic processes.
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Suitable for beginners without experience Contains resources for download Practice based on real cases Modern materials from MBA programs
Who is a data analyst?
A data analyst works with large amounts of information: collecting, cleaning and analyzing it, identifying patterns and trends. His main task is to turn data into understandable reports and insights that help businesses make decisions.
Who this course is for
Marketer
Master methods of evaluating the effectiveness of promotion channels and learn how to optimize your advertising budget.
Marketer
Master methods of evaluating the effectiveness of promotion channels and learn how to optimize your advertising budget.
Marketer
Master methods of evaluating the effectiveness of promotion channels and learn how to optimize your advertising budget.
Marketer
Master methods of evaluating the effectiveness of promotion channels and learn how to optimize your advertising budget.
Reasons to become a data analyst
1
Active growth in demand for specialists
According to research, the need for data analysts will double by 2025 and triple in industries such as IT and retail.
2
A Simple Start in IT
Even without a technical background, you can start a career in IT by mastering working with data and finding patterns in it.
3
Freedom of career growth
A data analyst is in demand in any industry, which allows you to choose your own directions and areas for professional development.
4
Diversity of work formats
You can work remotely, combine several projects as a freelancer or choose an office format.
In this course, you will master the
skills of a professional data analyst
Skills
  • Development of models for predicting customer behavior
  • I analyze data to find key insights
  • Process and structure information
  • Create visual charts and reports for data visualization.
  • I work with large amounts of information
  • Presentation of analysis results in an understandable and visually convenient format
  • Evaluation of the effectiveness of data-driven solutions
  • Building forecasts based on analytics
  • Segment the audience and develop targeted advertising strategies
  • Optimize marketing campaigns using data
Course Author
A Simple Start in IT
  • PhD in Engineering
  • 15 years of university teaching experience
  • 7 years in data analytics and artificial intelligence
  • 5 years of teaching courses on data analysis, statistics and machine learning
  • Developed machine learning models (decision trees, gradient bousting, neural networks) to solve business problems
  • Participated in projects on demand forecasting and creation of recommendation systems in Lamoda and SberMarket
  • Author of educational materials on data science available on Coursera and Stepik
  • Researcher at INRIA (France), specializing in machine learning and big data analysis.
Course supervisors
Valery Zverev
Lead Data Analyst at Raiffeisen Bank
Ekaterina Smirnova
Analyst at Ozon, ex-MTS
Dmitry Shef
Data Scientist at Telegram
Olga Ivanova
Head of Analytics at Google
Master theoretical knowledge
and immediately apply it in practice
Access to the course
Study the materials at your convenience from anywhere in the world.
Practical assignments
Master professional data analytics tools and develop your skills by applying them in real-life situations.
Course Program
Module 1: Prologue
  • About the course. A few words for “dummies”.
  • Introduction from the author
  • Just came by to ask?
  • How are we going to learn?
Module 2: Different caveats
  • Features of analytics in socio-economic professions
  • Model
  • Intuition and analytics
  • Statistician. Metrics. KPIs and Analytics
  • On maturity levels of the analytics function in a company
  • What is forward-looking analytics
  • Toolkit (Excel, SPSS, Statistics, R...and PSPP)
  • The "Dry Residue" of the section
  • OUTCOME: a quiz on the outcome of the section
Module 3: Introduction to Statistical Analysis - Basic Concepts of Statistics
  • Statistical Analysis
  • Sampling and the General Population
  • Calculating Sample Size
  • Variables
  • Scales of Variables
  • Hypotheses
  • Probability
  • Normal Distribution
  • Section Summary
  • Again ... TEST!
Module 4: Forming a Data Array
  • What is a Data Array
  • Realizing an Array
  • Preparing Data for Analysis
  • Can you "repair" a sample in an array if it is not very representative?
Module 5: Excel Midquel
  • Almost All Business is Excel
  • Basic Stuff
  • Summary Tables
  • IF
  • VPR
  • Handbooks
  • Summarizing Excel
Module 6: Descriptive statistics
  • The essence of descriptive statistics
  • Frequency distribution
  • Interesting frequencies
  • Complex case: representativeness and sample size
  • Mean
  • A case about the mean (instead of calculating it in Excel)
  • Median and mode
  • Minimum and maximum
  • Percentiles/persentiles, quartiles and deciles
  • A simple but complex case...
  • A story about a couple more averages ...
  • Measures of dispersion (variability, variation)
  • "Bias" of data distribution (differences from normal distribution)
  • Outliers
  • Is there any way to represent all this in a compact way?
  • Summary of descriptive statistics
  • Short complex task: to master all descriptive statistics
Module 7: Analytical Statistics
  • Once again about the peculiarities of socio-economic reality
  • Main difference from descriptive statistics
  • Three main blocks of problems solved by analytics
  • Back to basics: hypotheses and probability
  • Excel is good - but we will move on to another program
  • 2-minute introduction to PSPP
  • First steps in PSPP - descriptive statistics
  • Checking for normal distributions
Module 8: Analytical statistics — comparing differences between groups (samples)
  • What is it used for?
  • Once again about the significance of differences
  • Dependent (paired, linked) and independent samples
  • Basic tool: cross-classification tables (conjugate, summary)
  • Comparing independent samples
  • Comparing dependent (paired, linked) samples
  • Analyzing sequences
  • Continuing with sequences: a bit about "time series"
  • Comparing samples/groups in "big strokes"
  • To consolidate...
  • Practice comparing groups
Module 9: Analytical Statistics — hidden relationships between variables
  • Dependent and independent variables
  • Correlations
  • And again about what a meaningful statistical relationship is
  • Correlation of variables
  • Ambiguous correlations...
  • Regression
  • Regression equation assignment
  • Factor analysis
  • Factor analysis case
  • Reliability-Consistency analysis
  • Independent work with Cronbach's Alpha
  • Summary of the section on finding hidden relationships between variables
Module 10: Analytical statistics — object/case classifications
  • What is this used for?
  • Logistic regression
  • ROC analysis
  • Cluster analysis
  • Personnel clustering with your own hands
  • Overview of other methods: discriminant analysis, decision trees, support vectors
  • Ensembles? In Data Science? Ensembles of methods and data
  • Bird's-eye view of classification results
Module 11: Novel Overview — Machine Learning, Big Data, Artificial Intelligence
  • Let's talk about new concepts
  • Big Data / Big Data
  • Artificial Intelligent
  • Machine Learning / Machine Learning
  • Neural Network / Neural Networks
  • What is worth learning when drawing a line in this new language?
Module 12: The whole course in one slide
  • The course in 2 minutes
  • Here we are through the whole course
Bonus Lecture
  • Bonus 2 – Gift from Evgeny Ksenchuk: the book "Systemic Thinking"
Choose a plan that suits your goals
Introductory
$3
  • Training program - 2 modules
  • Lecture materials
  • No feedback
  • Course access - 1 week
  • Without a certificate
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Basic
$11
  • Training program - 11 modules
  • Lecture materials
  • Practical assignments
  • Student chat
  • Independent work
  • No feedback
  • Access to the course for 1 month
  • No certificate
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Standard
$28
  • Training program - 12 modules
  • Bonus lecture
  • Lecture materials
  • Practical assignments
  • Chat for students
  • Feedback with the mentor
  • Course access for 6 months
  • Certificate
Start now
Standard Plus
$37
  • The training program includes 12 modules
  • Bonus lecture
  • Bonus 2 - The book "System Thinking"
  • Lecture materials
  • Practical assignments
  • Chat for students
  • Mentor support
  • Assignment check and recommendations
  • Course access for 12 months
  • Certificate
Start now
Corporate
$320
  • Groups of 5 to 10 people
  • Training program - 12 modules
  • Bonus lecture
  • Bonus 2 - The book "System Thinking"
  • Participation in a corporate project
  • Mentoring
  • Access to course materials for 12 months
  • Adding to the group chat
  • Certificate
Start now
Offer for companies
You can train your employees on this course. We will adapt the program to your business.

If you train several employees at once, you will get a more favorable price.
Certificate of
Professional
Development
Get an official document confirming your new skills. It will become a strong argument for career growth and professional development.
Feedback from alumni
Yesenia
The course was very useful and structured. I especially liked the way the material is presented from simple to complex: first the basics of descriptive statistics, then the transition to analytical methods. Examples from real life helped to better understand how to apply the knowledge in practice.
Nick
Great course for those who want to understand data analysis. I liked the emphasis on practical tasks: analyzing differences between groups, classification and finding relationships. PSPP was a pleasant discovery - a simple and powerful tool to work with.
Anna
The course exceeded expectations! The material is presented in an accessible way, even for beginners. It is especially valuable that not only theoretical aspects are considered, but also real cases. Now I feel more confident in working with data.
Arthur
I really liked the balance between theory and practice. The course gives a clear understanding of how to solve statistical and predictive problems. PSPP proved to be a handy tool that is easy to learn. I recommend it to anyone who wants to dive into analytics.
Mariam
The course was a great start for me in the world of analytics. I liked the fact that the basic concepts are covered first, and then complex methods. Examples from life made the training even more interesting. Now I can apply what I have learned in my work.
We'll refund your
money if the course
doesn't work for you
If you decide within 5 days that the course is not right for you, we will refund the full cost of the course. If you decide later, we will refund the amount less the cost of lessons completed.
Questions
What are the requirements to get started?
No special skills or experience are required - the course is designed to start from scratch. All you need is a computer and a willingness to put in the time to learn.
How long does the course last?
You can learn at your own pace as there is no strict time limit. We recommend 3-5 hours per week, which is the optimal rhythm for successful completion of the course.
I have no experience in IT. Will I be able to understand it?
Of course! The course material is presented in a simple and structured way, which will allow you to gradually learn all the necessary skills, even if you have never experienced IT before.
Will there be feedback from the course tutors?
Yes, the course tutors will scrutinize your practical work and provide detailed comments to help you improve your results.
Can I get a refund if I change my mind about taking the course?
We aim to be flexible and take your circumstances into account. Refunds are available in full or in part as stipulated in the terms of the contract.