Data Analyst Compact Course

Start your career as a Data Analyst, with our Data Analyst compact course and learn everything you need to work in the big data business.

Data Analyst Compact course Fulltime:

Duration, content, temporal conception

Duration of the Bootcamp: 4 weeks
Monday – Friday: 9 am – 6 pm (GMT)
Total: 180 learning units
*1 LU = 45 min

Learning units

Subjects

General understanding of technology

Python

Databases, models and visualizations

Green IT

Practice phases

180

Total learning units

Data Analyst Compact course Parttime:

Duration, content, temporal conception

Duration of the Bootcamp: 12 weeks
Tuesday | Wednesday | Thursday: 4 pm – 7.15 pm = 4 learning units
Every second saturday: 9 am – 12.30 pm = 4 learning units
Total: 168 learning units
*1 LU = 45 min

Learning units

Subjects

General understanding of technology

Python

Databases, models and visualizations

Green IT

Practice phases

168

Total learning units

More about the course

This continuing education course provides participants with hands-on content and user-related skills in the subject area of the Data Analyst. The Data Analyst uses data to answer questions and communicate results to make business decisions. The course compactly presents various content and skills needed to meet the professional requirements of a Junior Data Analyst.

This course is especially designed for people who have an affinity in the field of mathematics, statistics, computer science or natural sciences and who have no previous experience with programming or the Python programming language. The course prepares for the role of Data Analyst and its tasks as well as challenges.

The following modules form the basis of the knowledge transfer:

Exercises accompanying the lessons (independent)
Team-oriented group work
Practical examples and hands-on exercises
Learning success checks

Hybrid training (On-site classroom training or virtual face-to-face teaching via Teams or similar VC (Virtual Classroom).

Bootcap prepayment: If you pay your boot camp in full in advance, you will receive an 8% discount on the seminar fee.

Bootcamp installments: To reduce your liquidity burden, we offer you to pay the boot camp in three monthly installments.

Bootcamp schoolarship: We work with a network of business companies. They are interested in hiring newly qualified graduates as trainees or junior consultants and – assuming the interview is successful – they will contribute part or all of the seminar fees.

Education Voucher (Bildungsgutschein): If you are unemployed or threatened by unemployment, you can – if you have a German residence permit – get up to 100% of the costs subsidized through an education voucher. (Bildungsgutschein).

You should have basic IT skills – such as the use of office products as well as the internet. Furthermore, the participants should be proficient in English.

Upon successful completion, participants will receive a certificate from Greenbootcamps listing the course content and grading, if applicable.

Participants will work in the with the trainer’s materials that will be distributed during the course.

MS Office, Internet access, advanced open-source applications (such as MS Power BI Desktop, Hadoop, Anaconda), learning management system, online communication system.

The training is accompanied by job application support measures. The aim is to enable participants to gain access to employment in the BI sector.

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Next starting date Parttime Course: 27.11.2023 (GMT)

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Next starting date Fulltime Course: 09.10.2023 (GMT)

Teaching content

Data Analyst compact course fulltime: 180 Learning unit in total, 1 LU = 45 min

Data Analyst compact course parttime: 168 Learning unit in total, 1 LU = 45 min

Data Analyst Compact Course

Learning unit (LU)

General understanding of technology

42 (fulltime)
42 (parttime)

Intro on DA, DS, DE

2 (fulltime)
2 (parttime)

Mathematics & Statistics

Data structures
Functions and graphs
Factorial
Probability
Modulus
Logic propositions and Truth tables
Sets and algebraic laws on sets
Scalars/ Vectors and matrix operations

10 (fulltime)
10 (parttime)

Descriptive Statistics

mean, median, mode, max, min, std. dev, variance and the graphs
Errors: RMSE, MAE, MSE…
Distributions and plots
Chi-square
Correlation
Eigen values/vectors
t-test (hypothesis testing)
ANOVA (Analysis Of Variance)
OLS regression

10 (fulltime)
10 (parttime)

Calculus

Differential calculus
Integral calculus
Mean Value theorem
Continuity, differentiability
Partial differentiation

8 (fulltime)
8 (parttime)

Microsoft Excel, Word, PowerPoint

4 (fulltime)
4 (parttime)

Intro to Python

4 (fulltime)
4 (parttime)

Intro to ML

4 (fulltime)
4 (parttime)

Python training session

38 (fulltime)
38 (parttime)

Python Programming (Basics)

Intro to Jupiter lab
Coding best practices
Basics:
– Numeric variable types
– Strings
– If statement (if, elif, else)
– Loops (while, for)
Data Structures in Python
– Lists
– Sets
– Mutability
– Dictionaries
– Comprehension

16 (fulltime)
16 (parttime)

Python Programming (Advanced)

Functions
– Introduction to functions
– Function definitions
– Calling functions
– Lambda functions
– Function challenges
Introduction to pandas
– Pandas functionalities
– Pandas visualization
– Exercise on functionalities and visualizations
– More exercise using EDA
– Dataframes and combining dataframes
Introduction to numpy
– Numpy functionalities
– Exercise on Numpy functionalities
– Dealing with Data and Time data
Data visualization
– Matplotlib
– Seaborn

22 (fulltime)
22 (parttime)

Databases, Models und Visualizations

40 (fulltime)
40 (parttime)

SQL

8 (fulltime)
8 (parttime)

EDA and modelling

Concepts of regression and classification models
Evaluation matrices for regression and classification
Implement them vs use Python packages

12 (fulltime)
12 (parttime)

Git

4 (fulltime)
4 (parttime)

Tableau

8 (fulltime)
8 (parttime)

Apache Airflow

4 (fulltime)
4 (parttime)

Spark

4 (fulltime)
4 (parttime)

Green IT

16 (fulltime)
16 (parttime)

Practice days spread over the course

44 (fulltime)
32 (parttime)

Sum of learning units

180 (fulltime)
168 (parttime)

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Once you click apply, our admissions team will contact you to arrange a call to answer your questions and explain our application process. if you have any questions feel free to contact us.