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:

Duration, content, temporal conception

Compact cours fulltime:

Duration of the Bootcamp: 6 weeks
Monday – Friday: 9 am – 6 pm (GMT)
Total: 168 learning units
*1 LU = 45 min 

Compact course parttime:

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
20
General understanding of technology
48
Python
52
Databases, models and visualizations
16
Green IT
32
Practice phases
168
Total learning units

More about the course

Compact 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.

Data Analyst Compact Course

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: 18.09.2023 (GMT)

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Next starting date Fulltime Course: 24.07.2023 (GMT)

Teaching content

168 Learning unit in total, 1 LU = 45 min 

Data Analyst Compact Course
Learning unit (LU)
General understanding of technology
42
Intro on DA, DS, DE
2
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
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
Calculus
  • Differential calculus
  • Integral calculus
  • Mean Value theorem
  • Continuity, differentiability
  • Partial differentiation
8
Microsoft Excel, Word, PowerPoint
4
Intro to Python
4
Intro to ML
4
Python training session
38
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
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
Databases, Models und Visualizations
40
SQL
8
EDA and modelling
  • Concepts of regression and classification models
  • Evaluation matrices for regression and classification
  • Implement them vs use Python packages
12
Git
4
Tableau
8
Apache Airflow
4
Spark
4
Green IT
16
Practice days spread over the course
32
Sum of learning units
168

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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.