144 Hours of Structured Learning
Build your capabilities through a paced program with time to understand concepts, practice techniques, and review your work.
- 48 learning sessions
- 3 hours per session
- Guided learning and independent practice
INDIVIDUAL TRAINING DATA SCIENCE
Turn Data into Clearer Decisions.
Build practical data science skills through Python, SQL, statistics, and machine learning. Learn to prepare data, investigate meaningful questions, communicate insights, and develop a project using real-world datasets.
Practical skills. Purposeful technology. Real-world application.
THE PROGRAM
Build the Skills to Work with Data from Start to Finish.
Follow a progressive learning journey from analytical thinking and Python fundamentals to data preparation, SQL, statistics, visualization, and machine learning. Apply each stage through guided exercises and milestone projects before bringing the skills together in a final data science project.
A Progressive Data Science Journey
Develop skills in the right sequence so every new method builds on the understanding and practice that came before it.
- Python, SQL, and data preparation
- Statistics and exploratory analysis
- Visualization and machine learning
Learn by Investigating Real Questions
Practice with realistic datasets and develop a final project that explains the question, analytical process, findings, and limitations.
- Hands-on data labs and checkpoints
- Analysis and model evaluation
- Portfolio-ready final project
LEARNING OUTCOMES
Move from Raw Data to Defensible Insights.
Develop the practical workflow to collect, prepare, explore, analyze, and communicate data. Learn to evaluate machine learning results thoughtfully rather than treating a model output as an answer by itself.
01
Frame Useful Data Questions
Translate a practical problem into clear questions that data can help investigate.
- Define the problem and decision context
- Identify relevant data and assumptions
02
Work with Python for Data
Use notebooks and common Python libraries to explore, transform, and analyze datasets.
- Python, Jupyter, NumPy, and pandas
- Readable and reproducible analysis
03
Prepare and Query Data
Improve data quality, work with relational datasets, and prepare a reliable basis for analysis.
- Handle missing values and duplicates
- Query and combine data with SQL
04
Explore Data and Explain Patterns
Use statistical thinking and visualization to identify patterns, anomalies, and relationships worth investigating.
- Descriptive statistics and EDA
- Charts and data storytelling
05
Build and Evaluate Baseline Models
Understand introductory machine learning workflows and assess model performance using metrics that fit the problem.
- Regression, classification, and clustering
- Validation, metrics, and limitations
06
Present an End-to-End Project
Bring the workflow together in a documented project that communicates methods, results, and responsible next steps.
- Reproducible notebook and visual output
- Clear explanation of findings and caveats
PROGRAM CURRICULUM
From Data Foundations to Applied Machine Learning.
Explore the modules, learning objectives, topics, and subtopics that guide the program. Each stage builds on the previous one, with practical checkpoints before moving to the next level.
Module Learning Objective
Understand the data science lifecycle, connect analysis to a real decision or problem, identify suitable data sources, and work responsibly with data and analytical tools.
- Distinguish data analytics, data science, and machine learning
- Understand the typical data science workflow from question to communication
- Explore examples across sales, operations, finance, and customer experience
- Recognize descriptive, diagnostic, predictive, and prescriptive questions
- Translate a broad business issue into an answerable question
- Identify the user, decision, context, and desired outcome
- Define assumptions, scope, and measures of success
- Separate correlation, explanation, and prediction
- Understand tabular, time-based, text, and categorical data
- Identify common sources such as CSV files, databases, and APIs
- Recognize incomplete, inconsistent, duplicated, and biased data
- Consider privacy, consent, access, and responsible use
- Set up or access Python and Jupyter notebooks
- Navigate notebooks, cells, outputs, and markdown notes
- Understand files, folders, packages, and environments
- Use Git and GitHub at an introductory level to track work
- Choose a practical case and define the problem statement
- List the questions, fields, and data sources needed
- Write assumptions and identify possible data risks
- Submit a concise analysis brief before moving to coding
Module Learning Objective
Write and understand basic Python code, use core data structures and functions, and complete small analytical tasks in a notebook before relying on data libraries.
- Use variables, numbers, strings, booleans, and None
- Apply arithmetic, comparison, and logical operators
- Convert between common data types
- Read tracebacks and fix basic syntax or runtime errors
- Use if, elif, and else for decision logic
- Repeat operations with for loops and range
- Use functions, parameters, and return values
- Break a task into small steps and test outputs
- Create, access, update, and iterate through lists
- Represent labelled values with dictionaries
- Work with tuples and sets at an introductory level
- Choose a useful structure for records and categories
- Run notebook cells in a controlled order
- Inspect values and intermediate results
- Use comments and markdown to explain an analysis
- Reproduce, isolate, fix, and retest an error
- Load a small structured sample into Python
- Calculate counts, totals, and simple summary measures
- Use conditions and functions to answer defined questions
- Explain the solution and pass the Python fundamentals checkpoint
Module Learning Objective
Load datasets, inspect their structure, clean common quality issues, transform columns, combine tables, and produce a documented analysis-ready dataset.
- Understand arrays and vectorized operations
- Perform basic arithmetic and summary calculations
- Work with shapes, indexing, and filtering
- Recognize when array operations are more suitable than manual loops
- Load CSV and spreadsheet data into a DataFrame
- Inspect shape, columns, data types, and sample rows
- Select rows and columns with labels and conditions
- Sort, rename, create, and remove columns safely
- Detect and handle missing values with an explicit rationale
- Identify duplicates, invalid values, and inconsistent categories
- Convert data types and parse dates
- Document cleaning decisions and avoid silently changing meaning
- Create derived features and normalized categories
- Aggregate with groupby and summary functions
- Combine tables with merge and concatenate
- Check keys, row counts, and unexpected changes after joins
- Profile a messy dataset and list data quality issues
- Apply a cleaning plan using pandas
- Validate key fields and key summary figures
- Submit a reproducible notebook with a clean data output
Module Learning Objective
Retrieve and combine relational data with SQL, summarize records for analysis, and connect database results to a Python workflow while checking the accuracy of the output.
- Understand tables, records, fields, and data types
- Read primary keys, foreign keys, and table relationships
- Distinguish structured data from files used for analysis
- Inspect a simple entity relationship diagram
- Use SELECT, WHERE, ORDER BY, and LIMIT
- Filter using comparison, logical, and pattern conditions
- Summarize data with COUNT, SUM, AVG, MIN, and MAX
- Group records and filter summaries with GROUP BY and HAVING
- Understand INNER JOIN and LEFT JOIN
- Combine customer, product, transaction, or event tables
- Recognize duplicate rows caused by join relationships
- Validate join results using counts and sample records
- Read and write simple subqueries
- Use common table expressions to organize a query
- Create date-based and category-based summaries
- Use clear aliases and format queries for maintainability
- Identify which preparation should happen in SQL or Python
- Load query results into a notebook where appropriate
- Reconcile SQL aggregates against Python summaries
- Checkpoint 4: deliver a verified query-based analysis
Module Learning Objective
Use descriptive statistics, distributions, sampling concepts, and exploratory data analysis to understand a dataset, examine relationships, and distinguish evidence from assumptions.
- Interpret mean, median, mode, range, variance, and standard deviation
- Understand percentiles, quartiles, and interquartile range
- Choose summary measures based on the data distribution
- Explain what summary statistics do and do not reveal
- Read common distribution shapes and spread
- Understand samples, populations, and sampling bias
- Recognize outliers and investigate before removing them
- Explain uncertainty without overstating a conclusion
- Explore relationships between numeric and categorical variables
- Interpret correlation and avoid causal claims from correlation alone
- Segment data to reveal meaningful differences
- Identify confounders and alternative explanations
- Frame a simple null and alternative hypothesis
- Understand p-values and confidence intervals conceptually
- Review assumptions behind a basic statistical test
- Report practical significance alongside statistical evidence
- Start with a question and data-quality review
- Inspect distributions and missingness
- Generate and test hypotheses using summaries and plots
- Write an EDA summary with findings, questions, and limitations
Module Learning Objective
Select appropriate charts, build clear visualizations, organize evidence into an analytical story, and communicate results in a way that supports a specific audience and decision.
- Choose charts for comparison, trends, distributions, and relationships
- Use labels, scales, units, annotations, and legends clearly
- Avoid misleading axes, clutter, and unnecessary decoration
- Consider accessibility and visual interpretation
- Build bar, line, histogram, box, and scatter plots
- Compare categories and inspect distributions
- Format titles, axes, legends, and chart layouts
- Add context and annotations that help answer the question
- Define a small set of meaningful measures and KPIs
- Organize charts around common business questions
- Understand filters, segments, and dashboard context
- Check consistency between dashboard visuals and source data
- Structure a narrative around question, evidence, and implication
- Distinguish observation, interpretation, and recommendation
- Adapt technical depth to the audience
- State uncertainty and avoid causal claims not supported by data
- Create a concise exploratory report using charts
- Present the three most important evidence-backed findings
- Explain data limitations and what still needs validation
- Receive feedback and revise the narrative
Module Learning Objective
Understand introductory supervised and unsupervised learning, prepare data for modeling, train baseline models, select relevant evaluation metrics, and interpret performance with appropriate caution.
- Distinguish regression, classification, and clustering problems
- Define target variables and baseline expectations
- Understand training, validation, and test data
- Recognize data leakage and why it causes misleading results
- Encode categorical fields and scale numeric features when needed
- Handle missing values within a reproducible workflow
- Create useful features based on domain context
- Use pipelines to keep preprocessing consistent
- Fit a simple linear regression model
- Understand logistic regression and tree-based classifiers
- Compare a model against a simple baseline
- Use scikit-learn estimators and a consistent modeling workflow
- Interpret MAE, RMSE, accuracy, precision, recall, and F1 as appropriate
- Understand confusion matrices and class imbalance
- Recognize overfitting, underfitting, and cross-validation
- Explain trade-offs and assess whether a model is useful for the stated problem
- Understand clustering and explore a basic K-means workflow
- Interpret clusters as exploratory groupings rather than guaranteed truths
- Review fairness, privacy, explainability, and consequences of errors
- Document model assumptions, limits, and recommended human oversight
Module Learning Objective
Complete a scoped end-to-end data science project that moves from problem framing and dataset assessment through cleaning, exploration, visualization, and an appropriately evaluated model where relevant.
- Choose a realistic question and intended audience
- Define scope, success measures, and analytical approach
- Review available data, access, quality, and privacy
- Prepare a project plan and initial data dictionary
- Create a reproducible data-loading and cleaning workflow
- Use Python and SQL where appropriate for the dataset
- Run EDA and test the most useful hypotheses
- Track changes and verify important figures
- Decide whether machine learning is justified for the question
- Build and compare a suitable baseline when relevant
- Evaluate the result with appropriate metrics and validation
- Explain model limitations or use deeper analysis if modeling is unnecessary
- Create a report or presentation with purposeful visualizations
- Explain findings, practical implications, and uncertainty
- Separate findings from assumptions and recommendations
- Prepare a short stakeholder presentation and answer questions
- Organize notebooks, data notes, README, and project files
- Remove credentials and sensitive personal information from shared artifacts
- Review code, data handling, and the evidence behind conclusions
- Present the final project and reflect on next learning steps
HOW YOU'LL LEARN
Understand the Question. Trust the Evidence.
Learn through guided explanations, notebook-based labs, data investigations, milestone reviews, and a final project. The aim is not only to produce charts or models, but to understand what the evidence supports and communicate it responsibly.
01
Understand
Build your foundation in analytical thinking, data concepts, Python, SQL, and the reasoning behind each method.
02
Practice
Work through data cleaning, analysis, visualization, and modeling exercises with feedback at key checkpoints.
03
Apply
Bring your work together in a documented project, then explain your methods, findings, limitations, and next steps.
Ask better questions. Find meaning in data. Communicate with clarity.
PROGRAM INFORMATION
Build a Practical Data Science Foundation.
A structured program for learners who want to develop their ability to analyze data, evaluate evidence, and create useful analytical work through practice and feedback.
01
Learning Commitment
A paced learning plan with dedicated time for guided sessions, hands-on work, and project development.
- 48 learning sessions
- 3 hours per session
- 144 total learning hours
- Guided learning and practice
02
Core Data Skills
Work across the essential tools and methods used in a practical data science workflow.
- Python, NumPy, and pandas
- SQL and relational data
- Statistics and visualization
- Machine learning fundamentals
03
Practical Milestones
Track progress through checkpoints that validate skills before you move to the next stage.
- Python and data preparation exercises
- SQL and EDA checkpoints
- Visualization and model reviews
- Final project presentation
04
Your Next Step
Use your project and feedback to identify strengths and decide which data specialty to explore next.
- Review your analysis and assumptions
- Reflect on model quality and limitations
- Organize project work for your portfolio
- Identify skills for continued learning
FREQUENTLY ASKED QUESTIONS
Before You Start Working with Data.
Learn more about the program structure, prerequisite skills, tools, projects, and how the learning journey progresses.
Still have questions?
Let's discuss your goals and whether this learning path fits your current experience.
Talk to Our TeamHow long is the program?
The proposed program structure consists of 48 sessions, with 3 hours per session, for a total of 144 learning hours.
The curriculum progresses from analytical thinking and Python fundamentals to data preparation, SQL, statistics, visualization, machine learning, and a final project.
Do I need prior data science or programming experience?
The curriculum is designed to start with foundational concepts and introductory Python. Prior data science experience is not assumed.
Comfort using a computer and a willingness to practice logical and quantitative problem-solving will help. The team can discuss readiness with you before enrollment.
Which tools and technologies will I learn?
The core workflow uses Python, Jupyter notebooks, NumPy, pandas, SQL, Matplotlib, Seaborn, and scikit-learn. Git and GitHub are introduced to help document and track project work.
Tools are introduced when they become useful to the learning task, rather than all at once.
Will I learn machine learning?
Yes. Machine learning is introduced after learners have practiced Python, data preparation, SQL, statistics, and exploratory analysis.
The program covers introductory regression, classification, clustering, model evaluation, and common limitations such as overfitting and data leakage. It focuses on understanding and evaluating baseline models rather than promising advanced specialization in every model family.
What will I build during the program?
You will complete practical exercises and milestone submissions involving Python, data cleaning, SQL, exploratory analysis, visualization, and introductory modeling.
The final project brings these skills together in a documented workflow with a clear question, verified data preparation, evidence-backed findings, and an explanation of limitations. Machine learning is used when it is appropriate for the question.
How is learning progress evaluated?
Progress is evaluated through hands-on exercises, milestone checkpoints, notebook reviews, analytical explanations, and the final project presentation.
Learners are expected to explain their decisions and evidence, not only submit code or charts. Assessment criteria should be shared before each milestone.
YOUR NEXT STEP STARTS HERE
Ready to Explore What Data Can Tell You?
Develop the skills to prepare, analyze, and communicate data through structured practice and a practical final project. Talk to our team to discuss your learning goals and next step.