Applied Statistical Methods for Data Science and Analysis
Introductory Course on Statistics with Certificate & Mentorship
Understand key concepts like frequency interpretation, measures of central tendency (mean, median, mode), sampling, and hypothesis testing. Master data preparation, cleaning, and handling missing data, and explore data analysis approaches for social science, big data, and data mining.
Grasp data visualization using tools like PowerBI, Tableau, and Excel, and gain experience with SPSS, JASP, and multivariate analysis techniques. Learn how to communicate data insights through infographics, reports, and presentations.
As businesses increasingly rely on data-driven decisions, this knowledge enables more accurate forecasting, trend identification, and problem-solving, meeting the growing demand for data professionals.
- Statistics Basics: Learn key concepts like central tendency, sampling, and hypothesis testing.
- Data Preparation: Explore data profiling, cleaning, and handling missing data.
- Analysis Techniques: Apply methods for big data, social sciences, and multivariate analysis.
- Visualization & Tools: Use PowerBI, Tableau, JASP, SPSS, and Excel for data analysis.
- Effective Communication: Present insights through reports, dashboards, and infographics.
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1.Introduction to Data Analysis and Statistics, Data Sources, Data Types
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1. Lektion
Introduction - Purpose of Training
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2. Lektion
Case Study 1
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3. Lektion
Case Study 2
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4. Lektion
What is Data Science About?
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5. Lektion
How to Extract Business Value from Data?
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6. Lektion
What Questions Cannot Be Answered by Data?
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7. Lektion
Complexity of Data Science, Emerging Issues - Data Distortion
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8. Lektion
Possible Data Sources
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9. Lektion
Own Questionnaire Research
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10. Lektion
Own Questionnaire - Composition, Questions
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11. Lektion
Usable Data, Structure of Data Table
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12. Lektion
Data - Variables
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13. Lektion
Text Data
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14. Lektion
Date-Type Data
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15. Lektion
Flags or Dummy Variables
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16. Lektion
Categorical Variables
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17. Lektion
Continuous Variables, Transforming Variables
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18. Lektion
[Homework] - Week 1
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19. Lektion
[Test] - Week 1
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20. Lektion
[Presentation] - Week 1
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2.Data Analysis Approaches, Data Preparation for Analysis
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21. Lektion
Familiarization with JASP Software, Installation
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22. Lektion
Familiarization with Data Table
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23. Lektion
Structure of JASP Program
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24. Lektion
Descriptive Statistics, Frequency
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25. Lektion
Adding Diagrams
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26. Lektion
Creating New Variables
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27. Lektion
Recoding
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28. Lektion
More on Recoding
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29. Lektion
Transforming Variables
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30. Lektion
Transforming Continuous Variables
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31. Lektion
Importance of Good Queries
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32. Lektion
Filtering Options and Importance
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33. Lektion
Problem of Missing Data
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34. Lektion
Handling Outliers
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35. Lektion
Saving Our Work
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36. Lektion
[Homework] - Week 2
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37. Lektion
[Test] - Week 2
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38. Lektion
[Presentation] - Week 2
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3.Basic Statistical Methods, Data Presentation, Data Visualization
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39. Lektion
Interpreting Frequencies
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40. Lektion
Central Tendency Indicators, Mean, Standard Deviation, Median, Mode
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41. Lektion
Sampling
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42. Lektion
Logic of Hypothesis Testing
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43. Lektion
Nature of Causality
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44. Lektion
How to Investigate Causality? Important Things to Know
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45. Lektion
Interpretation Traps
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46. Lektion
Cross-Table Analysis
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47. Lektion
Cross-Table Analysis in Practice
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48. Lektion
Comparing Means (ANOVA)
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49. Lektion
Correlation Analysis
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50. Lektion
Summary of Statistical Methods
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51. Lektion
Exporting Tables, Diagrams from JASP
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52. Lektion
Data Presentation, Data Visualization, Key Diagram Types
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53. Lektion
Emerging Issues with Diagrams
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54. Lektion
Dynamic, Interactive Dashboards
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55. Lektion
[Homework] - Week 3
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56. Lektion
[Test] - Week 3
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57. Lektion
[Presentation] - Week 3
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4.Tools for Data Analysis, Introduction to Multivariate Analysis
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58. Lektion
Introduction to Tools
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59. Lektion
Microsoft Excel - Strengths, Weaknesses
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60. Lektion
Excel in Practice 1
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61. Lektion
Excel in Practice 2
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62. Lektion
SQL, Example Database Schema
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63. Lektion
PowerBI - Strengths, Weaknesses
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64. Lektion
PowerBI in Practice 1
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65. Lektion
PowerBI in Practice 2
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66. Lektion
PowerBI in Practice 3
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67. Lektion
Python and R Programming Languages, JASP and Similar Software
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68. Lektion
Introduction to Multivariate Analysis
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69. Lektion
Transforming Variables - True/False to 0/1 Values
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70. Lektion
Linear Regression
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71. Lektion
Linear Regression in Practice 1
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72. Lektion
Linear Regression in Practice 2
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73. Lektion
Logistic Regression
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74. Lektion
Cluster Analysis
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75. Lektion
Decision Trees
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76. Lektion
[Homework] - Week 4
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77. Lektion
[Test] - Week 4
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78. Lektion
[Presentation] - Week 4
- The course will be held in English as well as all projects and questions will be submitted in English.
- E-learning materials, self-paced: Access interactive digital materials and guided coding videos to study at your own pace, with one year of rewatching available.
- Learn-by-doing approach, weekly schedule: Apply your knowledge through weekly practice exercises, requiring 8-12 hours of study each week.
- Constant mentoring, live sessions: Receive feedback on projects, ask questions anytime, and join live sessions for personalized support.
- Exam, certificate: Complete an exam and/or hand in your final project at the end of the course to earn a certificate for your CV and LinkedIn profile.
Edmond GirasekTrainer:in
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Marcel RüfenachtBildungsberater:in
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