Advanced Data Analytics (Part 1-6 of 6)

The purpose of our course is to help experienced analysts expand their competencies and set the right direction for further development in the profession. The practical experience gained will allow you to deepen your knowledge in the field of product analytics and learn how to select the right tools to solve problems in the most uncertain conditions and unfamiliar industries.

COURSE PROGRAM ://

PRODUCT APPROACH TO REPORTING CREATION

Dashboard development - one of the most popular requests to an analyst from the team. Often this does not solve the customer's problem. And without understanding why a dashboard was created, it most likely will not be in demand among the customer.

This problem can be avoided by mastering a product approach to creating dashboards through the use of a BI system. This way, the specialist will learn to offer quick alternative solutions or create a reporting system that meets business needs.

DESCRIPTION OF THE MODULAR PROJECT

Consists of 2 parts - according to the final task of each block (mini-projects). You will have to assemble a DashBoard Map project and create a dashboard within a BI system for a specific task, receiving feedback from course experts.

WORKING WITH THE DWH TEAM AND PROCESSING BIG DATA

In corporations, the analyst needs to communicate with analytical warehouse (DWH) specialists. To do this, it is important to understand what types of storage facilities there are, how to work with them, and how exactly the company answers the question of proper data storage.

And in small companies, analysts can write data processing pipelines on their own, so you need to know the most popular and optimal processing tools Big Data.

DESCRIPTION OF THE MODULAR PROJECT

There is a single final project of the module, which involves the use of all the studied tools in the module: using spark, we read data from S3 and CH, we carry out transformations (filtering, aggregation, joins, etc.) to obtain a report for recording in CH.

ADVANCED EXPERIMENTS

How to evaluate the impact of changes in companies on key business metrics? With the help of experiments, of course! The higher the level of the analyst, the more complex designs he can design, as well as speed up their implementation, analyze the results and take into account the specifics of specific metrics when choosing ways to evaluate changes.

Middle analyst knows how to go beyond the use of routine A/B tests, respond answer complex customer questions and increase the importance of experiments for making company decisions.

DESCRIPTION OF THE MODULAR PROJECT

The assessment for the module is based on work with situational cases and mini- projects on real data for each block, where it is necessary to solve a problem or apply a learned tool. Block 1 - case test, Block 2 - 7 mini-projects and a case test, Block 3 - 6 mini-projects.

MACHINE LEARNING FOR SOLVING ANALYTICS PROBLEMS

To solve non-trivial problems, an analyst will most likely have to go beyond the usual tools, so in this module we will get acquainted with advanced machine learning methods.

WHAT IS NEEDED FOR THE COURSE [?]

• Knowledge of basic Python syntax (loops, functions, conditional statements)
• Knowledge of libraries (pandas, numpy, scipy) at the level data import, export, preprocessing, EDA, basic work with random variables
• Visualization skills in Python (Seaborn, matplotlib building basic visualizations)
• Experience in analyzing simple experiments (t-test or Mann -Whitney in Python)
• Writing queries with JOIN, where, group by and aggregation functions
• Hypothesis testing
• Type 1 and 2 errors< /li>
• Statistical tests and p-value
• CLT
• Correlation
• Experience with Tableau, Power BI, Superset or other similar tools