Data Analysis I with Python

Course Overview: This course is designed to introduce participants to essential data analysis techniques using Python. It covers fundamental Python programming concepts, data manipulation, visualization, and statistical analysis. By the end of this course, students will be proficient in analyzing datasets, running statistical tests, and interpreting results using Python’s versatile libraries such as NumPy, Pandas, Matplotlib, and SciPy.

Course Outline:

1. Introduction to Data Analysis

• Understanding the significance of data analysis in decision-making.
• Overview of types of data and analytical approaches.

2. Data Analysis Tools

• Introduction to Python as a data analysis tool.
• Overview of Python libraries: Pandas, NumPy, SciPy, Matplotlib, and Seaborn.

3. Introduction to Python Programming Language

• Basics of Python syntax and structure.
• Overview of Python programming principles.

4. Setting Up Python Environment

• Installation of Python and required libraries.
• Introduction to IDEs (Jupyter Notebook, Spyder, PyCharm).

5. Python Coding Concepts

• Variables, data types, and operators in Python.
• Control structures and loops.
• Functions and modular programming.

6. Python Plotting and Graphs

• Introduction to data visualization with Matplotlib and Seaborn.
• Creating bar charts, line graphs, histograms, and scatter plots.

7. Python Normality Test

• Introduction to normal distribution.
• Conducting normality tests (Shapiro-Wilk, Anderson-Darling, Kolmogorov-Smirnov tests).

8. Python Homogeneity Test

• Understanding variance and homogeneity in data.
• Conducting Levene’s Test and Bartlett’s Test.

9. Transforming Non-Normal Data into Normally Distributed Data

• Data transformation techniques: log, square root, and Box-Cox transformations.
• Visualizing transformations and checking normality.

10. Python Correlation

• Introduction to correlation concepts (Pearson, Spearman, Kendall).
• Using Python to compute and visualize correlations.

11. Python Regression

• Simple and multiple linear regression in Python.
• Interpreting regression output (coefficients, p-values, R-squared).
• Model evaluation metrics.

12. Parametric Tests

• Understanding parametric tests and when to use them.
• Conducting tests in Python:
• One-sample t-test
• Paired t-test
• Independent t-test
• ANOVA (Analysis of Variance)

13. Non-Parametric Tests

• Introduction to non-parametric tests.
• Conducting tests in Python:
• Mann-Whitney U test
• Wilcoxon signed-rank test
• Kruskal-Wallis test
• Friedman test

Data Analysis II with Python

Course Overview: This course dives deeper into statistical modeling and analysis using Python, focusing on advanced methods such as ANCOVA, factorial ANOVA, repeated measures, and logistic regression. Participants will also explore AI tools for scientific research to expand their data analysis capabilities.

Course Outline:

1. Independent and Dependent Variables

• Understanding the roles of independent and dependent variables in statistical models.
• Identifying these variables in different experimental setups.

2. Analysis of Covariance (ANCOVA) Model Test

• Introduction to ANCOVA and when to apply it.
• Conducting ANCOVA in Python to control for covariates while testing main effects.

3. Factorial ANOVA Model Test

• Explanation of factorial ANOVA for examining interactions between factors.
• Implementing two-way and three-way ANOVA models using Python.

4. Repeated Measures Design – One-Way Repeated-Measures ANOVA Test

• Introduction to repeated measures design.
• Running one-way repeated-measures ANOVA in Python.
• Understanding within-subject factors and interpreting results.

5. Mixed Design ANOVA Model Test

• Combining between-subject and within-subject factors.
• Implementing mixed-design ANOVA using Python.

6. Log Linear Analysis for Several Categorical Variables

• Introduction to log-linear analysis for categorical data.
• Building models for several categorical variables in Python.

7. Multivariate Analysis of Variance (MANOVA) Model Test

• Overview of MANOVA for testing the influence of independent variables on multiple dependent variables.
• Conducting MANOVA using Python and interpreting multivariate results.

8. Repeated Measures Design – Two-Way Repeated-Measures ANOVA Test

• Expanding on one-way repeated measures to include two-way repeated measures ANOVA.
• Implementing this technique in Python and analyzing complex repeated measures designs.

9. Logistic Regression (Binary and Multinomial)

• Understanding binary and multinomial logistic regression.
• Applying logistic regression models in Python to predict categorical outcomes.
• Interpretation of odds ratios and model diagnostics.

10. AI Tools for Scientific Research

• Overview of AI and machine learning tools for enhancing scientific research.
• Using Python-based AI tools for predictive modeling, pattern recognition, and data classification.Certification

  The certificate will be provided by Beder International University after the student completes the course and passes the final examination conducted by Beder University

  Program Details:

  Hadaba si aad koorsadan u dhigato waa inaad marka hore iska diwaan gelisaa platform ka arday ahaan, kadib markaa aad is qortaa barnaamijka adoo taabanaya Enroll meesha ay ku qorantahay oo bixinaaya lacagta fadhiisinka ah. Intaa marka aad samayso ayaa koorsadan oo dhamaystiran oo duuban aad ka helaysaa dashboard ka kuu gaar ka ah ee shaashada kaaga muuqda. Marka aad koorsada dhamayso waxaad soo dalbanaysaa imtixaan si aad shahaado u hesho. Phone: 252637832783

   

  Categories	Description
  Total weeks	Two Weeks
  Course Name	Data Analysis with Python
  Medium of instruction	Somali (100%)
  Class Type	Online
  Course fee	$ 5
  Certificate fee	$ 30