Spring 2024: CS 2020/DASE 2020 Introduction to Statistics for Data Analytics

[Course Information] [Logistics] [Syllabus and Course Schedule ]

Course Information

Instructor: Oluwatosin Oluwadare Ph.D.

Teaching Assistant: Melkamu Mersha (PhD Student)

Time and Location

Course Description

This course is an introduction to statistics for data analytics using R Studio. Includes basic statistics, distributions, regression, statistical tests, variance and hypothesis testing (t-, chi^2, F tests), and ANOVA, an introduction to statistics and basic probabilities.

Student Learning Outcomes

Stat4DataAnalytics Image
After successfully completing the course, students will be able to: perform regression analysis, hypothesis testing, confidence intervals, Chi-squared tests, using statistics to understand data and an improved efficiency in using the R programming language. Using statistics for efficient data analytics. The pipeline for the course will follow the learning pipeline above (image from the class textbook).




Main Text Book for the course:
  • Ismay, Chester, and Albert Y. Kim. Statistical inference via data science: A ModernDive into R and the tidyverse. Chapman and Hall/CRC, 2019. Get an electronic copy from the UCCS Library.
  • The authors have also uploaded a frequently update open source copy of the textbook here: https://moderndive.com/ . This course is organized to closely follow this text book chapters.


    Course materials are available through TAAP (Textbook Affordability and Access Program). Log into your portal through uccs.textbookx.com; you can review your personalized value sheet to see if the TAAP membership benefits you. There is the option to TAAP out if the program does not fit your needs.
  • TAAP Materials Pick-up: Course materials can be picked up at the Campus Store unless you opted for home delivery.
  • More information: uccs.edu/TAAP or uccs.textbookx.com
  • Troubleshooting: Backordered items, TAAPing out, deadlines, returns, etc. Email: [email protected]

  • Reference Materials:


    Note: The final letter grades will be based on the curve of students' performace.


  • To be successful in this course, you (the student) need to attend every lecture. Students are required to attend lectures. Attendance is Mandatory
  • There will be in-class discussions and question asking, as necessary, for the class chapter covered. The problem sets will be assigned as labs in the class.
  • Class Absence or not participating will take a toll on your grade for the course. To be clear, attending class and not participating is the same as Absence for the day.
  • All the key class notes interaction will be on the whiteboard.
  • Bring your personal computer to the class.


    All announcements will be made on Canvas and also posted on the course website.

    Assignments, Deadlines and Late submission

    Midterm Exam

    Course Project


    Regrading request must be made within 7 days after we post scores on Canvas. Instructor or TA will handle regrade requests. If student is not satisfied with the regarding results, you get 7 days to request again. The instructor will regrade, and the decision is final.

    Course Policies

    Academic Help

    UCCS Policies


    For additional help, please reach out to your faculty members. UCCS has an expansive variety of support services and resources. Your success is important to us.

  • Syllabus and Course Schedule

    As the instructor for this course, I reserve the right to adjust this schedule in any way that serves the educational needs of the students enrolled in this course. - Oluwatosin Oluwadare

    Acronyms: HomeWork = HW, Course Project = CP
    Date # Lecture Topic Assignment Lecture Notes and Textbook Chapter Reading Extra Reading
    Out Due
    Fri, Jan 19 1 Course Overview and Introduction to Statistics for Data Analytics (R, Rstudio, R Packages) [PPT] & Chapter 1 Giorgi et al., 2022: Read Section 2 for History of the R Programming Language
    Fri, Jan 26 X Data Structure in R (I) & Data Visualization Canceled: Snow Day
    Fri, Feb 02 2 Data Structure in R (I) & Data Visualization HW01 [PPT] & Chapter 2 - 2.5 PS02_pre_lecture on ggplots
    Fri, Feb 09 3 Data Structure in R (II) & Data Visualization (cont'd) HW01 [PPT] & Chapter 2.6 - 2.9 booksales.csv data , salaries.csv, Other Salaries file type
    Fri, Feb 16 4 Data Wrangling and Tidying
    See make up Video Recording
    HW02 [PPT] & Chapter 3 - 3.5 & 4.4 Wide data.csv , Lecture 4 Discussion Slides
    Fri, Feb 23 5 Summary Statistics & Regression I: Regression with one Numerical Variable HW02 [PPT] & Chapter 5 - 5.1.2 Lecture 5 Rdata
    Fri, Mar 01 6 Regression I (cont'd) HW03 [PPT] & Chapter 5.3.1, 5.1.3 & 5.3.2 Section 5.2
    Fri, Mar 08 7 Sampling Distribution & Regression II: Regression with one Categorical Variable HW03 [PPT] & Chapter 5.2 - 5.2.3 Chapter 7 for sampling distribution
    Fri, Mar 15 X Multiple Regression Canceled: Snow Day CP Proposal Proposal Format Template
    Fri, Mar 15 X Confidence intervals and the bootstrap & class reviewCanceled: Snow Day
    Fri, Mar 22 8 & 9 Multiple Regression & Confidence intervals and the bootstrap [PPT] & Chapter 6
    [PPT] & Chapter 8
    Fri, Mar 29 X Spring Break: No class CP
    (Mon: 4/3)
    CP Proposal
    (Mon: 4/3)
    Fri, Apr 05 10 Midterm Exam
    Fri, Apr 12
    11 Hypothesis Testing I HW04 [PPT]
    Fri, Apr 19 12 Hypothesis Testing II (z-test & t-test) & Hypothesis Testing III (Types of t-tests) HW04 [PPT] Hypothesis Testing Rdata, standard normal table, t distribution table
    Fri, Apr 26 13 Hypothesis Testing IV (Chi-Square tests) [PPT] Table of the chi square distribution , ChiSquare Example_Rdata
    Fri, May 03 14 Hypothesis Testing V (ANOVA Test) & Inference from Regression CP [PPT]
    Fri, May 09 X Finals Week

    Oluwatosin Oluwadare,PhD @ UCCS © 2024. All rights reserved.