Data Analysis with Python and SQL

Data Analysis with Python and SQL

The classroom (offline) course

10.06.2024 - 12.08.2024
01.07.2024 - 04.09.2024

The online course

10.06.2024 - 12.08.2024
01.07.2024 - 04.09.2024

We teach you how to solve real problems using data analysis

Data analysis is not only the processing of information after it has been received and collected but also a means of testing hypotheses and making decisions. The goal of any data analysis is to understand the entire situation under study (identifying trends and negative deviations from the plan, obtaining recommendations and forecasts). This is achieved through the main tasks of data analysis:

  • gathering information,
  • structuring information,
  • identification of patterns and analysis,
  • forecasting and receiving recommendations.
  • 12 weeks of classroom work

    In terms of the volume of information and the number of technologies and tools mastered, this course firmly holds first place among others..

    Please note: for this course, in some cases we may conduct a pre-test (the test is free).

    In addition to the standard Python libraries, you will also get an idea of additional libraries (Numpy, Pandas) used in data analytics and Data Science. .

    Over 20 tools and 50 competencies

    The course provides a large amount of knowledge in several areas at once:

  • General principles of data analysis
  • Programming with Python
  • Working with SQL Data Query Language
  • Working with Jupiter Notebook
  • The required amount of knowledge in the field of mathematical analysis, probability theory, and mathematical statistics.
  • Actual cases for the first portfolio

    Despite the fact that the course contains a fairly large amount of theory, the course will include at least 5 types of problems with real data sets, which are most often encountered in the work of a data analyst:

  • Calculation of investment strategy (predictive analysis)
  • Development of an effective recommendation system (predictive analysis)
  • Optimization of enterprise marketing costs (intelligence analysis)
  • Maximizing company profits (predictive analysis)
  • Analysis of real estate market trends (descriptive analysis)
  • Optimization of product range (predictive analysis)
  • At the end of the course you will be able to:

  • prepare data for analysis using Python and SQL;
  • structure information;
  • formulate hypotheses and test them using mathematical statistics methods;
  • generate reports with initial recommendations using modern visualization tools;
  • continue your career in Data Science and Machine Learning;
  • Everything is included in the course price

    The courses take place in the center of Tallinn, at Tartu mnt. 18. Group size is up to 8 people

    All training materials are included in the course price.

    If necessary, a laptop is provided for the duration of training.

    Please note: from June 2020, this course can be taken as part of our cooperation with Eesti Töötukassa.

    Free consultation:

    The Estonian Unemployment fund (Eesti Töötukassa)
    Employer / Company
    Self payment
    I agree to receive news and special offers from Gamma Intelligence (no more than once a month)
    I do not agree to receive news and special offers from Gamma Intelligence

    Ask a question:

    If you have additional questions, please send us an email:

    Call us: (372)55581521

    Call us at 55581521 and we will answer all your questions.

    Course Description:

    Target group: Specialists involved in collecting, processing, and analyzing data.

    Lecturer: Maxim Kolodiev (Master's degree in computer and systems engineering)

    Training duration: 12 weeks

    Language: English

    Group size: up to 8 students

    Volume / Content: 94 academic hours (12 weeks)

    Price: 1967,21 EUR + VAT

    Requirements for students:

  • secondary education
  • understanding the basics of probability theory and mathematical statistics
  • confident PC user
  • English level A2/B1
  • It is desirable to have your own PC (Windows), if necessary, a computer will be provided for the duration of the training
  • At the end of the course, you will learn:

  • how to prepare data for analysis using Python and SQL;
  • how to structure information;
  • how to formulate hypotheses and test them using methods of mathematical statistics;
  • how generate reports with initial recommendations;
  • how continue your career in Data Science, Machine Learning;
  • Additional Information:

    Basic rules for organizing training (in Estonian)
    Basic rules for ensuring the quality of the educational process (in Estonian)
    Module Main module topics Duration
    Introduction to Data Analysis Modern problems solved by data analysis. Basic concepts in data analysis. Numeric and categorical data. A brief overview of data analysis tools. 4 ac. hours
    Introduction to the Python and Jupyter environment The Python interpreter. IDE. PIP package manager. Installation of iPython and Jupyter environment.Basics of using Jupyter Lab: cell types, navigation, shortcuts, installing extensions. Introducing Google Colab. 4 ac. hours
    Collections in Python Introduction and basic operations of data types: list, tuple, set and dictionary. 8 ac. hours
    The flow control in Python Construction of logical conditions. The loops. Practical work. 4 ac. hours
    Introduction to VCS/GIT Register on GitHub. Creating your own repository. How Git works. 12 ac. hours
    Practical part 2 ac. hours
    Introduction to NumPy module The concept of one-dimensional and multidimensional arrays, operations with arrays, changing data types in arrays, determining the memory footprint and speed of an operation, and a general overview of the capabilities of the NumPyodule. 4 ac. hours
    Probability and combinatorics Theoretical and experimental probability. Probability distribution. Bayes' theorem. Combinations and permutations. NumPy.random module for conducting experiments. Practical work. 4 ac. hours
    Introducing the Pandas Module Concepts of dataframe and series. Indexing. Dataset manipulation. Grouping. Obtaining statistical data. Merging datasets. Creating new columns 4 ac. hours
    Basic concepts of statistics Gaussian distribution. Constructing and testing hypotheses. Correlation. Determination of outliers. Basic types of charts. 6 ac. hours
    Data visualization Overview of Matplotlib, Seaborn, Plotly and Bokeh modules. Plotting charts: Bar Chart, Histogram, Boxplot, Scatter Plot, etc. Practical work. 8 ac. hours
    Practical part 2 ак. ч.
    SQL query language and MySQL DBMS nstalling and configuring the MySQL server. Creation of databases and tables. Data types. The concept of relational databases. Writing basic queries in SQL. 8 ac. hours
    Practical part Practice using SQL. 2 ac. hours
    Working in Pandas with different data sources Uploading data from csv, json, xlsx, xml, pdf, etc. Uploading a dataset from a MySQL database. Writing a script to create API requests. Saving the dataset in different formats. Practical work. 8 ac. hours
    Data cleaning inding missing data using a heat map, and replacing missing values. Working with outliers. Find and remove duplicates. Determining the relevance of features. Bringing data to a single format. Practical work 8 ac. hours
    Generating reports Analyst - as a link between IT and business. Full cycle of generating reports with specific recommendations for business. Practical work. 6 ac. hours