Data Analysis with Python and SQL
The classroom (offline) course
08.05.2024 - 09.07.2024
The online course
08.05.2024 - 09.07.2024We 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:
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:
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:At the end of the course you will be able to:
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.
Ask a question: training@gamma-intelligence.com
If you have additional questions, please send us an email: training@gamma-intelligence.com.
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:
At the end of the course, you will learn:
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 |