Data Analytics Career Overview - From Skills to Interviews

Explore how analytics roles split by domain and analytics focus, highlighting three roles—business analyst, product analyst, data scientist—for reporting, product behavior, ab testing, and causal inference.

Define objectives, allocate a 10k marketing budget, and map the funnel from awareness to conversion and loyalty while comparing online and offline channels to optimize ROI.

Analyze customer service workflows with data to identify common hardware and software issues, consult specialists, categorize solutions, and develop frequently asked questions and a chatbot to reduce tickets by 30–40%.

Explore people analysis to forecast attrition using tenure and indicators from employee experience survey, attendance, and complaints, then apply machine learning to identify three-month departure risk for human resources actions.

Learn how a business analyst builds weekly or monthly reports and dashboards using Excel, Google Sheets, or SQL, organizes metrics by importance, and derives insights from time series trends.

Analyze the ecommerce funnel from visit to checkout, measure conversion at each stage, identify pain points, and test improvements via a/b testing or staged rollout with statistical validation.

As a marketing data scientist, apply RFM segmentation or machine learning to automate customer grouping, integrate browse and cart behaviors, and AB test improvements to boost ROI and scalability.

Learn about four categories of analytic tools—spreadsheets, databases, visualization, and programming languages—and how traditional and tech-savvy companies use them across departments.

Understand the importance of basic SQL structure and core syntax for querying databases, and learn where SQL fits in data analysis and interviews, with learning resources.

Explore SQL basics with select and from by comparing to pivot table, using a sample Airbnb NYC dataset to show how to select the price column from sheet one.

Learn how the SQL where clause filters data, like a pivot table filter, by selecting records with price less than or equal to 20 to show only those under 20.

Learn group by and aggregation in SQL to compute average price by neighborhood, filtering Brooklyn or Manhattan, using summarize by average and count with group by.

Learn how to use order by in SQL to sort neighborhoods by average price from highest to lowest, comparing Manhattan and Brooklyn and choosing ascending or descending.

Learn how the entity relationship diagram (ERD) maps user and order relations and how to use joins and foreign keys to identify which registered in 2023 Q1 and made purchases.

Explore SQL joins: left join, right join, full outer join, and inner join, and how they match rows by a join key and condition, producing nulls when no join occurs.

Master the self join, a technique that joins a table to itself. Use the orders table with shifted dates and inner joins to detect users who order on consecutive days.

Explore SQL window functions, using over with partition by to rank rows per user id via row_number and to extract first or last values without using group by.

Learn SQL without experience by using free courses (including DataCamp) and practice on Google Cloud BigQuery, then build a portfolio with Kaggle and Medium to showcase your skills.

Explore how dashboards and data visualization with Tableau turn charts and metrics into actionable insights, comparing current and prior periods and enabling weekly or monthly views.

Learn why Python and R matter beyond Excel and SQL. Python and R enable scalable automation, data pipelines, and open source libraries to build models, automate segmentation.

Use Python, R, and pandas to read csv or form results, clean data, filter invalid emails, and prepare Salesforce-ready files via API.

Analyze Google Map reviews to detect irrelevant content, bad language, and readability issues, then create reusable Python and R functions to filter reviews and automate the workflow, saving team time.

Explore Python and R resources for analytics, including DataCamp tracks, Udemy data analytics with R, and Udacity nano-degree, to tackle real-world analytics questions and expand your toolset.

Explore quantitative analysis concepts, define metrics using product sense, tailor your analysis methodology, avoid common mistakes, and plan preanalysis and postanalysis steps.

Use a concise product sense framework to evaluate a product with metrics—weekly active users, time on site, and ads revenue per user—to separate traffic and revenue and show week-over-week trends.

Define metrics to evaluate the social media app’s performance, including weekly active users, time on site, user spend, and ad revenue per user.

Define daily, weekly, and monthly active users and measure engagement such as time on site, posts seen, likes, subscribes, follows, and ads conversion rate to optimize recommendations and ad outcomes.

Compare days on an e-commerce site by normalizing for different user counts and orders to assess performance. Evaluate sales, conversion, search results, recommendations, and user experience from different perspectives.

Define conversion rate as unique purchasers divided by total visits, and compare daily to year-over-year trends in traffic, orders, and funnel stages from search to add-to-cart, considering recommender system effects.

Learn how normalization corrects bias when evaluating metrics such as conversion rate and click through rate by using the right denominator and numerator, and presenting average order per user.

Explore normalization of email campaign data by calculating open rates across five campaigns with differing audiences, incorporating open and click events to derive a robust formula.

Calculate the normalization of the open rate by dividing total opens by total recipients, not by averaging per-email rates, to avoid bias when emails reach different numbers of users.

Compare two days with differing user and order counts by normalizing metrics to assess true performance across sales, conversion, search results, recommending system, and user experience.

Explore how the relationship between ad spend and return on ad spend is non-linear, requiring data-driven trend monitoring and understanding factors like recommender systems and bidding in a competitive landscape.

Practice a product case by evaluating smartphone sales from a large, established brand versus a startup, applying metrics and normalization to compare performance across price and release date.

Compare monthly traffic, search volume, page views, and conversion funnel for smartphones A and B; assess growth, revenue, and pros and cons to guide stakeholder decisions.

Explore recommended resources for data analytics careers, including case interview guides, statistics courses, and experimental methods. Learn from newsletters and LinkedIn thought leaders to stay updated on metrics and growth.

Explore the five interview question types—self-introduction, project experience, behavior, technical, and case—and see how they assess SQL, data manipulation, algorithms, and product sense through real daily work questions.

Learn to craft a tailored self introduction for interviews in 1–3 minutes by highlighting relevant skills, experiences, projects, and industry interests, with notes to guide follow-up questions.

Master behavioral interview questions by using the star framework to tell clear stories about teamwork, leadership, and communication, including situations, tasks, actions, results, and learning.

Master sql concepts such as left join, inner join, and outer join, and perform data manipulation with Python and R using reproducible pipelines and algorithms for analytics roles.

Discover how to define and query the best-selling product in 2023 January using two tables—orders and items—balancing sales and units, handling matches, and resolving ties in SQL interviews.

Demonstrates SQL with inner join of orders and items on item id, counting orders for January 2023, grouped by item name and ordered by count to reveal the top product.

Discover how to identify users who placed orders on multiple days using a self join on the orders table, focusing on order_id, user_id, item_id, and date of order.

Identify users who placed orders on more than one day by joining the orders table twice as o1 and o2, then group by user id to show unique users.

Learn core data manipulation interview topics—outlier removal, data cleaning, and data transformation—and model evaluation metrics like roc curves, auc, accuracy, precision, and recall, with practice resources.

Explore how algorithms test programming skills across data roles, from game-like tasks to a data pipeline. Practice on Udemy and Leetcode for machine learning engineer and data engineer interviews.

Demonstrate case interview skills by building an end-to-end analysis plan that identifies the problem, clarifies ambiguity, conducts quantitative analysis, and summarizes findings and recommendations to stakeholders.

Construct a methodology to identify the root cause of a sudden 30% sales drop, set assumptions, and analyze data to understand the impact in e-commerce case interviews.

Identify whether the observed drop is a data quality issue or a data issue, then trace through the funnel and data pipeline to locate the root cause.

Define metrics to measure the performance of each email in an e-commerce email marketing campaign. Use data analysis to optimize the campaign and drive initiatives.

Analyze email marketing metrics to measure engagement and revenue, including open rate, click-through rate, conversion rate, and unsubscribe rate, and apply optimization and ab testing to improve sales per user.

Explore how leadership can evaluate promotion impact over the long term using data analysis and alternative methodologies, contrasting short-term sales effects with long-term business value, and discuss a practical solution.

Explain holdout testing by comparing a 10% holdout group with the promotion 90% over a year, using hypothesis testing, p-values, and confidence intervals to assess sales and conversion rate.

Master case interview techniques to handle ambiguity by clarifying open-ended questions, asking questions, making assumptions, and exploring perspectives, demonstrated through a revenue-doubling scenario for a video streaming business.

Breaks down revenue into ads, video sales, and other streaming metrics, assesses growth trends, and identifies monetization options from ads and SEO to live streaming e-commerce commissions and premium subscriptions.

Clarify the objective and channels before allocating a 500k marketing budget in a case interview, then pause to think and review the approach with the interviewer.

Define a clear objective with the interviewer, analyze historical data by channel, allocate and test budgets across online and offline channels, and iteratively adjust to maximize ROI.

Develop a systematic analytics plan for advanced case interviews, and specify the methodology you will use with the reasoning behind each analysis. Evaluate results and adapt approach for other campaigns.

Analyze a case interview on allocating a 100k translation budget for an international OTA to maximize revenue by selecting languages and pages to translate, using data-driven reasoning.

Analyze historical data to compare traffic and conversion by language, region, and hotel, then test translations on high-traffic pages using a 10% AB budget.

Estimate the impact of a 2x price increase for a SaaS subscription using an analytics methodology, applying ambiguity handling techniques to present a data-driven case interview solution.

Use historical data to assess internal effects of price increases and blend external market research on competitors and price sensitivity with customer segmentation to predict cancellations.

Explore the thinking process for case interviews by evaluating machine learning versus behavior analysis for customer segmentation and personalized emails, then measure long-term impact with holdout testing.

learn a four-part thinking process for case interviews—objective, method, solution, and next steps. assess interviewer intent, reproducibility, and whether the effort yields meaningful impact.

Diagnose why SEO traffic fails to convert by building a funnel from visit to checkout with SQL, identify root causes, and improve search with AB testing and a daily dashboard.

Explore a curated set of interview resources for data analytics careers. Access behavioral questions from LinkedIn Learning and Muse, SQLpad practice, Python pandas, Leetcode algorithms, and analytics case materials.

Navigate offer negotiation by analyzing base salary, bonuses, and stock vesting, then leverage current compensation, cost of living, and data from levels.fyi to bid for better pay and benefits.

Celebrate finishing this data analytics course, and share your feedback via the Google form while connecting on LinkedIn for more resources and courses if there is interest.