Data Science with Python Programming
One of the most popular languages for automating data analysis with artificial intelligence and programming skills is Python. The training provides foundational knowledge in programming and algorithm development for data analysts. You will then learn various methodologies for data cleaning and analysis using fundamental analytics libraries. Throughout the course, you will apply the covered topics by performing analyses on real data. As a result, you will develop algorithmic thinking skills as a programmer and learn to make decisions using data as a data analyst.
Training table
Data Analytics with Python
Data Analytics with Python
Datenanalyse mit Python
Аналитика данных с помощью Python
Information about the training
Data analytics is the process of extracting significant meaning from accumulated data. In recent years, technological advancements (such as social networks, e-commerce projects, the Internet of Things, etc.) have led to an increase in data. This data growth, in turn, has paved the way for technological revolutions in areas such as artificial intelligence, self-driving vehicles, advancements in healthcare, economic forecasting, and more accurate recommendations in the entertainment sector. The training will cover topics including data types and functions in Python, RegEx, OOP principles, data mining, matrix analysis with Numpy, data manipulation with Pandas, correlations with Scipy, probability distributions, outlier detection, introduction to visualization with Matplotlib, advanced visualization with Seaborn, 3D visualization, and many more subjects.
Who is this training for?
Beginners in Data Science, individuals interested in data analysis using Python, programmers and data engineers, business managers, business analysts, data analysts, those learning data analytics and interested in the field, individuals who want to work with large datasets, researchers and students.
Certificate
Certificates will be awarded to individuals who successfully complete the training at the end of the course. You can see a sample certificate on the right.
Python Data Science Tutorial
Python for Data Science
Simplilearn
This Data Science with Python Tutorial will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas and more. Video Courtesy: Simplilearn.
Syllabus
Session 1
- Introduction to Python
- Data Types in Python
- Data Structures
- Mathematical Operators
- Logical Operators
- Creating and Using Functions
- Conditional Operators
Case Study 1
Creating a Calculator
Session 2
- Zip and Map Functions
- Lists
- Anonymous Functions and Filter
- Functional Operators
- Loops and Error Handling
Case Study 2
Application of Map and Lambda Functions on Data.
Session 3
- Introduction to Numpy
- Matrices and Linear Algebra
- Vector Operations
- Applying Mathematical Operations with the Numpy Library
- Exploring Matrices with Numpy
- Data Visualization using Seaborn and Matplotlib Libraries
Case Study 3
Application of the Numpy Library on Real Data.
Session 4
- Introduction to Regular Expressions
- Metacharacters in Regular Expressions
- Working with Regular Expression Functions
Case Study 4
Validating Email Addresses with RegEx.
Session 5
- Introduction to the Pandas Library
- Data Mining with Pandas
- Handling Missing Data
- Grouping and Aggregation Operations
- Application of the Pandas Library on Real Data.
Case Study 5
Real Data Manipulation and Data Cleaning
Trainers
Minura Huseynli
Head of Data Analytics and Reporting, Digital UmbrellaMinura Huseynli has over 8 years of professional experience in the data field. She currently works as the Head of Data Analytics and Reporting at Digital Umbrella. She has worked as a consultant on many local and international data science projects across different sectors. She has led teams of up to 10 people during project implementation. She currently provides data science services for several companies. She has extensive practical knowledge in advanced statistics, machine learning with Python, data management with SQL, visual analytics with Tableau and other BI programs, and obtaining strategic information from data.
Cəlal Rəhmanov
Data Elmi üzrə Ekspert, Kapital bank ASCWith over 4 years of experience in the field of data science, Jalal Rahmanov currently serves as a Data Science Expert at Kapital Bank’s Micro Business Tribe. He was previously a Data Scientist specializing in NLP and AI at Kapital Bank’s Center of Excellence team, where he contributed to international projects presented by foreign experts in European countries such as Germany.
Prior to this, he held the position of CVM and BI - Junior Data Scientist at Yelo Bank. Jalal has both onsite and remote work experience with local and international companies, including Azerbaijan Artificial Intelligence Laboratory, Pasha Bank, and The Sparks Foundation.
He possesses practical skills in the implementation and integration of technologies such as Python, Tableau, Dataiku, SQL, GitLab, Docker, SparkMLlib, and Kafka.
Emil Mirzəyev
SÜNİ İNTELLEKT VƏ STRATEJİ QƏRARQƏBULETMƏ RESEARCHER, UNİVERSİTY COLLEGE LONDONDr. Emil Mirzayev holds a dual PhD in Economics and Business Administration and is currently engaged in scientific research at the intersection of Artificial Intelligence and Strategic Decision-Making at University College London, one of the world's top universities. He has nearly 10 years of experience with Python and Data Science, and has presented his work at prestigious institutions like MIT, Harvard, and LBS, as well as at numerous international conferences. In Azerbaijan, he has also conducted multiple free workshops on topics related to Machine Learning and AI.