Data Scientist Job: Day-to-Day Work Inside a Company, See how Data Scientist work: Learn experiments, predictive models, Python, SQL, notebooks and data science team work.
What you’ll learn
-
Explain what a data scientist does at work and how the role differs from data analyst, analytics engineer, data engineer and ML engineer
-
Map a data science team: who owns product decisions, methods review, data contracts, production models and privacy review
-
Frame a business question with product partners as a decision, population, outcome, guardrail metrics and acceptance test
-
Follow a data scientist workday and week: planning, stand-up, methods review, experiment review and product decision readouts
-
Make Jupyter notebooks reproducible with Python, pandas and GitHub, and prepare data science work for peer review and handoff
-
Run exploratory data analysis (EDA) with SQL and Python to check grain, missing values, duplicates, distributions and event-time order
-
Design an A/B test with randomization, sample size and power, a primary metric, guardrail metrics and a decision rule set before launch
-
Forecast demand with time-aware validation, a seasonal naive baseline, forecast error and clearly communicated uncertainty
-
Separate prediction from causal inference and match every descriptive, predictive or causal claim to the evidence behind it
-
Build a feature dataset without data leakage and evaluate a machine learning model with precision, recall, calibration and a threshold
-
Detect and fix data leakage, experiment imbalance and broken notebooks through containment, rebuild, fresh review and clear communication
-
Plan your first 90 days as a data scientist and prepare for data scientist interview questions and practical exercises
Course Description
This course contains the use of artificial intelligence. Data Scientist Job: Day-to-Day Work Inside a Company is a practical course on how a data scientist works inside a real company. You will learn how to frame a business question with product partners, explore data with SQL and Python, design an A/B test, build a forecast, train and evaluate a predictive model, separate prediction from causal claims, and present evidence that supports a product decision.
The course follows Adam Vail, Data Scientist, a new joiner on the Decision Science and Experimentation team at Walnut Keystone Corporation, a fictional global online furniture retailer. You see one data science project from start to end: a lateness-risk model defines which delivery orders are eligible, and a randomized experiment tests whether a proactive delivery update reduces customer support contacts. Every step shows the document, query, notebook, evaluation table, experiment plan or decision readout that carries the work.
The data scientist role and team
- What a data scientist does at work, from an uncertain business question to checked evidence and a decision
- How the data scientist role differs from the data analyst, analytics engineer, data engineer and machine learning engineer, and why titles and boundaries vary between companies
- Who owns product decisions, methods review, data contracts, production models and privacy review, and how to reach them
- How to frame a business question as a decision, population, outcome, guardrails and acceptance test
The workday and the week
- How exploration, coding, reviews, meetings and written updates fit into a data scientist workday
- How planning, stand-up, methods review, experiment review and product decision readouts connect across the week
- How to make Jupyter notebooks reproducible, request peer review and hand work to another role
Core data science work
- Exploratory data analysis (EDA) with SQL and Python: grain, missing values, duplicates, distributions and event-time order
- A/B test design: randomization, hypotheses, sample size and power, primary metrics, guardrail metrics and a decision rule locked before launch
- Forecasting with time-aware validation, a seasonal naive baseline, forecast error and uncertainty
- Prediction versus causal questions, and how to match every claim to the evidence that supports it
A data science project from question to decision
- Write an approved analysis plan with a label, prediction time, eligible population, outcome and review gates
- Build a feature dataset without data leakage, with chronological train, validation and test periods
- Compare a baseline and a candidate model with precision, recall, calibration, slice checks and a decision threshold
- Analyze the experiment with effect size, confidence interval and guardrails, and present a decision readout
Tools, vocabulary and failures
- Python, pandas, JupyterLab and GitHub for reproducible analysis
- Snowflake, BigQuery, Databricks and dbt, and how warehouses, lakehouse notebooks and tested transformations fit together
- scikit-learn, statsmodels, Power BI, Tableau, Looker and experiment platforms, shown as tool categories without ranking
- The vocabulary of experiments, model evaluation, forecasting, workflow and causal inference
- How a team detects feature leakage, handles experiment imbalance and broken notebooks, and fixes them safely
Before day one and interviews
- Skills and portfolio projects to practice before your first data science job
- A first 90 days plan: learn the business, reproduce existing work, deliver reviewed evidence and own one improvement
- How to answer data scientist interview questions and approach practical coding, statistics, experiment and forecasting exercises
Practical tools you can use at work
Apply what you learn with downloadable PDF guides and Excel workbooks, including:
- Data Scientist Team Contact Map
- Meetings, Experiment Reviews, and Decision Readouts Guide
- Data Science Analysis and Model Work Templates
- Data Scientist Job Vocabulary
- Experiment and Model Incident Card
- Data Scientist Skills Self-Check and 90-Day Plan
- Data Scientist Interview Preparation Guide
- Experiment, Model, Forecast, and Tool Map
Who this course is for
- Students and graduates who want to see what a data scientist job involves day to day
- Data analysts, engineers and researchers moving toward applied data science
- Career switchers preparing for a first data scientist role
- New data scientists who want a clear picture of team workflows, reviews and handoffs
- Job seekers preparing for data scientist interviews and practical exercises
Who this course is for:
- Students and graduates who want to see what a data scientist job involves day to day
- Data analysts, engineers and researchers moving toward applied data science
- Career switchers preparing for a first data scientist role
- New data scientists who want a clear picture of team workflows, reviews and handoffs
- Job seekers preparing for data scientist interviews and practical exercises
