Data Analytics Classes Berlin

This course covers the skills needed to become a Data Analyst or Business Analyst, including data analytics fundamentals, visualization with Tableau, using Python to clean and manipulate data, and how to work with relational databases. One-on-one mentoring is included.

In this program, participants will learn the key skills and tools needed to succeed as a data analyst, including Excel, SQL, and Tableau. Attendees will learn how to use Excel to create graphs, summarize data with PivotTables, and analyze data. In SQL, attendees will learn the essentials of relational databases, queries, functions, and more. Finally, participants will learn Tableau to create visualizations to express their data. The program is open to beginners and has flexible scheduling options. Tuition includes hands-on training, a free retake, and training manuals for each course.

Learn to problem solve, and effectively communicate, like an analyst. This course teaches you to use industry-standard tools to make ethical, data-driven decisions. Experience hands-on training to master SQL, Excel, Tableau, PowerBI, and Python – tools listed in virtually every data analytics job posting across industries.

In this 8-day intensive Data Analytics bootcamp, students with little to no background in data analytics will learn how to use Microsoft Excel, SQL, and Tableau to summarize, display, and discover key insights hidden in data sets. By the end of this bootcamp, students will be able to use Excel to aggregate and summarize data, sort data with PivotTables, create charts and tables in Excel, use SQL to filter and sort large data sets, use advanced database queries, create visualizations with Tableau, and much more. Students will also be able to reinforce what they learn through the numerous projects they encounter throughout the course. This course also offers a free retake within six months to allow students to revisit any concepts needing to be refreshed and walk away with a much stronger grasp of the material.

In this comprehensive data science program, individuals will learn a variety of critical skills to become a data scientist. Students will learn to code with Python, create machine learning models, automate tasks like web scraping, and interact with databases using SQL. Topics include data analysis with Python's Numpy and Pandas packages, data visualization, predictive analytics, machine learning, SQL querying, Python automation, and web scraping. Students will learn hands-on by working in Python and SQL and will complete the program with the skills they need to enter the field of data science. This program offers flexible scheduling and provides a free retake for students to refresh the material.

This nine-week data analytics bootcamp offered in Berlin focuses in-depth on the uses of Python, Pandas, SQL, and create dashboards. Students will demonstrate their knowledge with a capstone project.

Students in this program will learn important concepts and tools for business analytics, machine learning, and data engineering included (but not limited to) Python, Tableau, and SQL. They'll also explore data collection, inferential statistics, and deep learning.

Python is used for a different project each week to teach students data analysis and machine learning. Topics in the course include Pandas, SciKit-Learn, model math, and Deep Learning.

The DSR curriculum covers three dimensions of data science: engineering, analytics, and business. Within each of these disciplines are included sub-topics ranging from Python and SQL to machine learning, probability and statistics, and technical and presentation skills.

This nine-week bootcamp gives participants the skills they need to join data science teams or accelerate their current career growth. Learn how to convert raw data into insightful information. Specific course topics include understanding basic statistical theories, using Python to write code, using seaborn and matplotlib to visualize data, using SQL to develop databases, publishing findings, data processing, using performance metrics, overfitting, unsupervised learning, managing images and text, understanding neural networks, developing computer vision, machine learning, and a capstone project.