Data Analysis with Python and SQL
About
Target group
- Specialists involved in collecting, processing, and analyzing data.
Learning outcome
- 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;
Training methods
- Total course volume: 180 academic hours, of which 94 academic hours are spent in the classroom (including practical classes (8 academic hours) and 2 seminars (8 academic hours))
Course information
Time of conduct
Course length
Training takes place in the center of Tallinn at Tartu mnt. 18. All educational materials are included in the course price. A laptop is provided
Format and place of conduct:span>
english
Training language
1,967.21 EUR + VAT
Price
94 ак. ч.
Total course volume
Course program
| Module | Main topics | Volume |
|---|---|---|
| 1. 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 |
| 2. 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 |
| 3. Collections in Python | Introduction and basic operations of data types: list, tuple, set and dictionary. | 8 ac. hours |
| 4. The flow control in Python | Construction of logical conditions. The loops. Practical work. | 4 ac. hours |
| 5. Introduction to VCS/GIT | Register on GitHub. Creating your own repository. How Git works. | 12 ac. hours |
| 6. Practical part | Practical part | 2 ac. hours |
| 7. 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 |
| 8. 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 |
| 9. Introducing the Pandas Module | Concepts of dataframe and series. Indexing. Dataset manipulation. Grouping. Obtaining statistical data. Merging datasets. Creating new columns | 4 ac. hours |
| 10. Basic concepts of statistics | Gaussian distribution. Constructing and testing hypotheses. Correlation. Determination of outliers. Basic types of charts. | 6 ac. hours |
| 11. Data visualization | Overview of Matplotlib, Seaborn, Plotly and Bokeh modules. Plotting charts: Bar Chart, Histogram, Boxplot, Scatter Plot, etc. Practical work. | 8 ac. hours |
| 12. Practical part | Practical part | 2 ac. hours |
| 13. SQL query language and MySQL DBMS | Installing 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 |
| 14. Practical part | Practice using SQL. | 0 ac. hours |
| 15. 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 |
| 16. Data cleaning | Finding 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 |
| 17. 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 |
Information about training in this course
Requirements for students:
- confident PC user
- proficiency in English sufficient for reading technical documentation (approximately corresponding to A2/B1 level)
- english level A2/B1
- It is desirable to have a personal laptop (Windows/Mac, 8 GB RAM, screen size > 13.3 inches); a laptop will be provided for the duration of the training if needed.
Evaluation criteria for learning outcomes:
- Learning outcomes are assessed based on independently completed practical work.
Evaluation methods:
- Upon successful completion, practical and homework assignments receive a "pass" grade.
Course completion conditions:
- To successfully complete the course and receive a certificate, it is necessary to achieve a "pass" grade on 75% of the homework assignments.
Additional information:
Ask a question:
training@gamma-intelligence.com
Call us:
(+372)55581521
Tutors
Roman Kutselepa
Gamma Intelligence OÜ lecturer
Qualification:
Over 5 years in software development. Specialization: web design, JavaScript development, effective use of software products in the company
Specialization:
web design, JavaScript development, effective use of software products in the company
Education:
Anglia Ruskin University 2010 (England)
Nikolai Zubrilov
Gamma Intelligence OÜ lecturer
Qualification:
Specialization:
Education:
Maksim Kolodijev
Gamma Intelligence OÜ lektor
Qualification:
Kvalifikatsioon: 15+ aastat kogemust tarkvaraarenduses, tarkvaratestimises, andmeanalüüsis 3+ aastat kogemust koolitajana ja konsultandina
Specialization:
Tarkvara arendusprotsess, tarkvara testimine, andmete analüüs
Education:
Haridus: TalTech, Master Degree (2007)