Live Online Programming for Data Science (Python & R) Training
Live online, instructor-led · next cohort Nov 2, 2026 · from USD 1,700
Coding skills let analysts automate repetitive work, handle data sets too large for spreadsheets and reproduce results on demand. Starting from installation and your first scripts in both Python and R, you build core data structures in each language: lists, dictionaries, NumPy arrays and pandas DataFrames in Python, and vectors, lists, factors and data frames in R. You then learn control flow, conditionals, loops and writing reusable functions. Data wrangling sessions use pandas, dplyr and tidyr to filter, sort, group, join, reshape and summarise real data.
Later sessions cover visualisation with matplotlib, seaborn and ggplot2, reading data from files and databases, and organising code into clear notebooks and scripts. Comparing the two languages side by side helps you choose the right tool for each task and read code written in either. Short exercises build into a small end-to-end analysis project. Analysts, researchers, M&E officers, finance and business professionals and graduates moving into data roles will find it a solid starting point. You finish able to write clean code that loads, cleans, analyses and visualises data in both languages.
At a Glance
How Live Online Training Works
Live online training, led by a trainer in real time, for participants anywhere.
More about live online training →- Format
- Live online, instructor-led
- Duration
- Mon - Fri (10 Days)
- Fee
- USD 1,700 (KES 160,000)
- Next start dates
- Nov 2, 2026, Nov 30, 2026, Dec 28, 2026
- Certificate
- Certificate of Completion
- What you need
- A computer with a reliable internet connection, a webcam and a headset
About this course
Python and R remain the two dominant languages in data science, each with strengths analysts need to draw on. This course builds working programming fluency in both — covering core syntax, data structures, control flow, and the libraries used daily for data wrangling and visualization.
Participants complete parallel exercises in Python and R, building the comparative fluency needed to choose the right tool for a given data science task and collaborate across teams that use either language.
Throughout the programme, participants work through realistic scenarios drawn from technology organizations, digital transformation programmes, data-driven enterprises, and public sector IT environments, examining how programming for data science (python & R) is applied in practice and where it most often breaks down. Delegates will develop practical skill in designing appropriate frameworks, selecting and applying the right tools for core data structures in r, interpreting findings, and communicating conclusions clearly to colleagues, management and external stakeholders. Sessions move deliberately from principles to application, so that each technique introduced is practised against a realistic problem before the programme moves on — strengthening both individual effectiveness and the wider capability of the organizations delegates return to.
Training Objectives
By the end of this course, participants will be able to:
- Develop robust programming for data science (python & R) frameworks that support successful implementation and measurable outcomes.
- Analyze performance using recognized evaluation methods drawn from core data structures in r.
- Design practical systems that strengthen accountability, transparency and evidence generation.
- Evaluate outcomes and assess longer-term impact across a range of operating contexts.
- Apply relevant tools, frameworks and analytical methodologies confidently and appropriately.
- Integrate data wrangling with pandas into existing organizational processes and reporting structures.
- Strengthen decision-making and operational effectiveness through evidence-based professional practice.
What you'll learn
Key Takeaways
By the end of the Programming for Data Science (Python & R) Training, participants will leave with:
- A comprehensive working understanding of programming for data science (python & R) principles and performance-measurement methodology.
- Enhanced ability to design practical systems and evaluate outcomes reliably.
- Improved competency in core data structures in r, reporting structures and analytical interpretation.
- Practical techniques for translating findings into strategic insight and clear recommendations.
- Greater confidence in applying programming for data science (python & R) and assessing measurable results.
- Stronger capability to support organizational learning and continuous performance improvement.
- Improved approaches to strengthening sustainability, accountability and operational effectiveness.
Training Methodology
This training course adopts a practical, participant-centered learning approach that builds programming for data science (python & R) expertise, strengthens analytical capability and develops confident professional judgement. The methodology combines specialist facilitation, applied hands-on experience and globally recognized professional practice, so that delegates return able to apply what they have learned immediately rather than in theory only.
Methodology includes:
- Expert-led sessions facilitated by experienced programming for data science (python & R) practitioners and subject-matter specialists.
- Practical application of the frameworks, tools and techniques used in core data structures in r.
- Real-world case studies drawn from technology organizations, digital transformation programmes, data-driven enterprises, and public sector IT environments.
- Tool-based demonstrations of the systems, templates and reporting applications used in current practice.
- Interactive discussion and scenario-based learning focused on realistic challenges and decision points.
- Structured group exercises with facilitator feedback, reinforcing learning through practice rather than lecture alone.
Day 1: Setting Up and Writing Your First Code in Python and R
- Installing and configuring Python, R, Jupyter and RStudio environments
- Scripts, notebooks and the console: when to use each
- Variables, assignment and basic data types in both languages
- Arithmetic, comparison and logical operators side by side
- Reading error messages and interpreting tracebacks
- Package installation with pip, conda, install.packages and library()
- Decision points and escalation routes in setting up and writing your first code in python and r
Day 2: Core Data Structures in Python
- Lists, tuples, sets and dictionaries and their use cases
- Indexing, slicing and nested structures
- Mutability, copying and reference behaviour
- String handling and formatting for data labels
- List and dictionary comprehensions for concise transformation
- Introduction to NumPy arrays and vectorised operations
- Technology and automation supporting core data structures in python
Day 3: Core Data Structures in R
- Vectors, factors, matrices, lists and data frames
- Vectorisation and recycling rules in R
- Subsetting with brackets, double brackets and the dollar operator
- Coercion rules and handling NA values
- Tibbles and the tidyverse data structure conventions
- Mapping R structures to their Python equivalents
- Common challenges, risks and failure points in core data structures in r
Day 4: Control Flow, Functions and Reusable Code
- Conditionals: if, else if and vectorised ifelse / np.where
- for and while loops, and when to avoid them
- Writing functions with arguments, defaults and return values
- Scope, environments and avoiding side effects
- apply family in R and map/lambda in Python
- Structuring scripts into reusable modules and sourced files
- Worked example: applying control flow, functions and reusable code in a live programming for data science (python & R) context
Day 5: Data Wrangling with pandas
- Series and DataFrame construction, inspection and indexing
- Filtering rows, selecting columns and conditional assignment
- Handling missing values, duplicates and inconsistent types
- groupby aggregation and pivot tables
- Merging, joining and concatenating DataFrames
- Reshaping with melt, stack and pivot
- Reviewing outcomes and capturing lessons learned
Day 6: Data Wrangling with dplyr and tidyr
- The pipe operator and chained transformations
- filter, select, mutate, arrange and summarise verbs
- group_by and grouped summaries across multiple columns
- Join families: inner, left, anti and semi joins
- pivot_longer and pivot_wider for tidy data
- Comparing dplyr and pandas approaches to the same task
- Standards, frameworks and good practice governing data wrangling with dplyr and tidyr
Day 7: Importing and Exporting Data
- Reading and writing CSV, Excel, JSON and fixed-width files
- Encoding, delimiter and date parsing issues
- Connecting to relational databases and running SQL queries from code
- Working with APIs and JSON responses
- Parquet and other columnar formats for larger datasets
- Writing results back to files, databases and shared locations
- Planning and sequencing importing and exporting data within the wider workflow
Day 8: Visualization with matplotlib, seaborn and ggplot2
- matplotlib figures, axes and plot customisation
- seaborn distributions, categorical plots and pair plots
- The grammar of graphics and ggplot2 layer structure
- Aesthetics, scales, facets and themes in ggplot2
- Choosing chart types for distributions, comparisons and trends
- Exporting publication-ready graphics at correct resolution
- Resourcing, cost and capacity considerations
Day 9: Debugging, Testing and Reliable Data Workflows
- Common error types: syntax, type, index and merge key errors
- Using print statements, breakpoints and debuggers in both languages
- Defensive coding and input validation for data pipelines
- Profiling slow code and reducing unnecessary loops
- Version control basics for analysis scripts
- Documenting code and producing reproducible notebooks and R Markdown
- Practical tools, templates and techniques for debugging, testing and reliable data workflows
Day 10: Choosing the Right Tool and Building an End-to-End Project
- Criteria for selecting Python or R for a given data task
- Interoperability: reticulate, feather and shared data formats
- Structuring a project from raw data to final output
- Applied exercise: import, clean, transform, visualise and export a dataset
- Peer review of code readability and structure
- Building a personal roadmap for continued library learning
- Documentation, record-keeping and reporting requirements
Programming for Data Science (Python & R) Training suits people who handle data daily but write little or no code, and those fluent in one language who need working competence in the other.
- Data Analysts and Reporting Analysts moving from spreadsheets to scripted workflows
- Junior Data Scientists consolidating Python and R fundamentals
- Business Intelligence Developers extending SQL skills into pandas and dplyr
- Statisticians and Research Analysts adopting reproducible scripting
- Database and Data Engineers who need scripted import, export and transformation routines
- Actuarial, Finance and Marketing Analysts building their own data pipelines
- Academic Researchers and Postgraduate Students preparing data for analysis and publication
Delegates who complete all ten days receive a Certificate of Completion from Vision Reach Global Consultancy, confirming their ability to write and debug Python and R code for data manipulation, apply pandas and dplyr to clean and transform datasets, and produce visualisations with matplotlib, seaborn and ggplot2.
Choose Where You Want to Learn
Programming for Data Science (Python & R) Training Venues
KenyaMombasaNext Nov 2· 13 dates$3,200- KenyaNaivashaNext Nov 9· 13 dates$3,000
- KenyaNairobiNext Nov 16· 13 dates$3,000
- KenyaKisumuNext Nov 2· 13 dates$3,000
- KenyaMachakosNext Nov 9· 13 dates$3,200
UgandaKampalaNext Nov 9· 13 dates$3,800
RwandaKigaliNext Nov 2· 13 dates$3,800
South AfricaCape TownNext Nov 2· 13 dates$7,600
TanzaniaZanzibarNext Nov 23· 13 dates$5,200
UAEDubaiNext Nov 2· 13 dates$7,600- South AfricaPretoriaNext Nov 9· 13 dates$6,400
- South AfricaJohannesburgNext Nov 9· 13 dates$6,800
- TanzaniaDodomaNext Nov 2· 13 dates$3,600
- TanzaniaDar es SalaamNext Nov 2· 13 dates$4,000
- TanzaniaArushaNext Nov 16· 13 dates$3,800
NigeriaAbujaNext Nov 16· 13 dates$5,600- NigeriaLagosNext Nov 23· 13 dates$5,600
GhanaAccraNext Nov 23· 13 dates$9,000
Scroll sideways to see all 18 cities →
Frequently Asked Questions About Live Online Programming for Data Science (Python & R) Training
Everything you need to know about this course before you register.
By the end of the Programming for Data Science (Python & R) Training programme, you'll be able to write functional python and r code for data manipulation tasks, use pandas and dplyr to clean, filter, and transform datasets, apply core programming concepts: loops, functions, and conditionals, and visualize data using matplotlib, seaborn, and ggplot2. The full breakdown of topics is covered session by session in the Course Outline tab above.
Programming for Data Science (Python & R) Training suits people who handle data daily but write little or no code, and those fluent in one language who need working competence in the other. Typical delegates include Data Analysts and Reporting Analysts moving from spreadsheets to scripted workflows, Junior Data Scientists consolidating Python and R fundamentals, Business Intelligence Developers extending SQL skills into pandas and dplyr, Statisticians and Research Analysts adopting reproducible scripting, Database and Data Engineers who need scripted import, export and transformation routines, Actuarial, Finance and Marketing Analysts building their own data pipelines, and Academic Researchers and Postgraduate Students preparing data for analysis and publication.
Programming for Data Science (Python & R) Training typically runs as 10 Days. It's available as classroom, live virtual, and in-house training — every course can also be delivered on-site for your team on dates that suit you.
Programming for Data Science (Python & R) Training is scheduled in-classroom in Mombasa, Kenya, Naivasha, Kenya, Nairobi, Kenya, and Kisumu, Kenya, and 14 other locations, plus a live interactive virtual classroom you can join from anywhere. Check the schedule panel above for exact upcoming dates and fees in each location.
The next live virtual cohort of Programming for Data Science (Python & R) Training starts November 2, 2026, with new classroom cohorts also running on a rolling basis. Pick a date and location in the schedule panel above, then click "Register for the Course" — it takes a few minutes and your seat is confirmed once payment or a signed purchase order is received.
Yes — delegates who meet the attendance requirement receive a Certificate of Completion for Programming for Data Science (Python & R) Training from Vision Reach Global Consultancy, issued in the name you register with, so double-check the spelling at checkout.
Programming for Data Science (Python & R) Training is pitched at foundation to intermediate professionals. If you're unsure whether it's the right fit for your current role or background, message our training advisors before you register and they'll help you confirm.
Fees for Programming for Data Science (Python & R) Training vary by delivery location and format and are shown in real time in the schedule panel above once you pick a date. Register 3 or more delegates on the same course together and a 5% team discount is applied automatically — larger cohorts can request a custom corporate quote.
Yes — Programming for Data Science (Python & R) Training can be delivered on-site at your offices (or virtually for distributed teams), with case studies and examples tailored to your industry and the specific challenges your team is working through. Switch to the "In-House" tab in the schedule panel above to request a proposal.
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