The “Ultimate Plan to Become a Data Scientist in 2016” was a real Analytics Vidhya roadmap by Kunal Jain, organized as a month-by-month curriculum intended to take a beginner to job applications by December 2016. It is best read now as a historical learning framework, not a current qualification, employment guarantee, or complete 2026 curriculum. The live article shows “Last Updated: 31 Jan, 2017,” while its exact original publication date is not clearly displayed. Read the original article.
What the 2016 roadmap was trying to solve
Jain’s article addressed a familiar beginner problem: too many courses, tools and competing opinions make it difficult to decide what to learn first. Rather than rank every available resource, it proposed a fixed sequence. The stated ambition was to help a learner become a data scientist by December 2016 “at a conservative pace.” That is the plan’s objective, not a verified outcome for everyone who followed it.
Analytics Vidhya later described it as a month-by-month career guide. Its value is mainly in sequencing: understand the work, build quantitative foundations, learn programming, practice machine learning, communicate results, then prepare for hiring.
The original roadmap at a glance
| Period | Original focus | Useful evidence of progress |
|---|---|---|
| January–February | Life of a data scientist and business analytics | Explain how data supports decisions and where the role fits |
| March–April | Statistics, algebra, probability, calculus and inference | Interpret uncertainty, assumptions and quantitative results |
| May | R and Python learning | Produce a reproducible analysis in a primary language |
| June–July | Machine learning and practice datasets | Build and evaluate baseline models without leakage |
| August | Visualization and business-intelligence tools | Communicate a finding through a clear chart or dashboard |
| September | Two data-science competitions | Document validation, feature choices and limitations |
| October | Hiring preparation | Target roles and prepare technical and behavioral evidence |
| November–December | Job applications | Apply across realistic entry-level and adjacent roles |
The complete resource list appears on Analytics Vidhya’s plan page.
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January and February: understand the work before studying tools
The first stage points beginners to “Life of a Data Scientist” and “Spectrum of Business Analytics.” This is a strong starting decision. Data science is not simply training algorithms; it includes framing a question, obtaining and cleaning data, choosing an appropriate method, communicating uncertainty and connecting an analysis to a decision.
A useful exit test is to describe one business problem, the data it would require, the decision the analysis would inform and the risks of a misleading result. The original roadmap names the orientation resources but does not define such a mastery check.
March and April: build mathematics and statistics foundations
The plan lists inferential and descriptive statistics, algebra, probability, multivariable calculus, data analysis and statistical inference, with material from Udacity, Khan Academy and Coursera. Completing a course, however, is not the same as being able to use the ideas.
By the end of this stage, a learner should be able to:
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- Explain sampling, selection bias, variance, confidence intervals and hypothesis tests.
- Use conditional probability and recognize common probability distributions.
- Understand vectors, matrices, projections and the linear-algebra ideas used in dimensionality reduction.
- Explain why derivatives and optimization matter when fitting models.
- Translate a statistical result into plain language without presenting correlation as causation.
The 2016 plan does not state weekly hours, assessment standards or a required depth. Those omissions matter: a learner working full time may need longer than two months, while someone with a quantitative degree may need a shorter review.
May: learn programming and analytical tools
The resource page combines swirl for R, Codecademy for Python, Hadley Wickham’s Advanced R and Dataquest’s Python material. The dual-language choice reflects 2016 practice, but learning both simultaneously can slow a complete beginner.
Choose a primary language
- Python first: a natural fit for general programming, automation, machine learning and production workflows.
- R first: a strong fit for statistical analysis and research-heavy work.
- Both: broad exposure, but greater cognitive load; add the second language after producing useful work in the first.
Language syntax is only one layer. The learner also needs data manipulation, reproducible notebooks or scripts, documentation, version control and basic testing. A meaningful May deliverable is a cleaned dataset and a reproducible exploratory report that records assumptions and missing-data decisions.
June and July: machine learning plus projects
The original stage recommends Andrew Ng’s machine-learning course, a loan-prediction dataset, classification and regression trees, clustering, the Titanic dataset, Yaser Abu-Mostafa’s Learning from Data and ensemble-modeling material.
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- Titanic is useful for learning, but its heavy reuse makes it weak evidence of original, workplace-level ability.
- Loan prediction raises real issues—class imbalance, missing values, leakage, fairness and changing population behavior—that a simple accuracy score cannot settle.
- CART and clustering are important techniques, not a complete machine-learning workflow.
- Ensembles should be evaluated for calibration, interpretability, validation design and operational constraints, not only leaderboard or holdout accuracy.
Every project should include a baseline, a justified train/test strategy, error analysis, feature reasoning, limitations and a plain-language conclusion. SQL, data modeling, APIs, deployment, monitoring and software engineering are not prominent in the visible monthly list and must be added for a modern target role.
August: visualization and business intelligence
The roadmap names QlikView, Tableau and D3.js. Including visualization before job applications is sensible: an analysis that cannot be understood cannot reliably support a decision.
Tool familiarity is less important than these transferable skills:
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- Choose a chart that matches the question and audience.
- Avoid misleading scales, clutter and unexplained aggregates.
- Show uncertainty where it affects interpretation.
- Move from raw data to finding to recommendation.
- Explain which business decision the dashboard or graphic supports.
The original plan does not explain why a beginner should learn three ecosystems. One well-designed dashboard and one concise analytical narrative are usually stronger portfolio evidence than shallow exposure to several products.
September: competitions as practice, not proof
The plan assigns two data-science competitions. Competition platforms can provide messy data, feature-engineering practice, validation feedback and a shareable artifact. They also reward leaderboard optimization, which is not the same as maintaining a model, working with stakeholders or making a responsible business recommendation.
For each competition, document:
- The problem definition and data provenance.
- The baseline and validation strategy.
- Feature choices and any leakage controls.
- Error analysis, limitations and reproducibility instructions.
- What a decision-maker should do with the result.
Include at least one non-competition project with an explicit user, policy question or business decision.
October: turn learning into hiring evidence
The roadmap points readers to a “Damn Good Hiring Guide.” Hiring preparation should cover more than a résumé. Decide which role you are pursuing: data analyst, product analyst, analytics engineer, data scientist or machine-learning engineer. Their interviews and expected evidence differ.
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- SQL and data extraction.
- Statistics, experimentation and model evaluation.
- Programming and debugging.
- Communication, product judgment and behavioral questions.
- Data quality, privacy, fairness and responsible use.
Translate projects into decisions, scope and measurable results where those results genuinely exist. Do not imply that a course certificate or competition rank is equivalent to professional impact.
November and December: applications are a beginning, not a guaranteed endpoint
The final stage directs learners toward job applications. The outcome depends on starting education, available time, portfolio quality, geography, work authorization, networking, role seniority and interview performance. Apply to realistic adjacent roles as well as jobs titled “data scientist.” Internships, apprenticeships, internal transfers and analyst positions can provide a more attainable entry point.
In a source-page response about advanced degrees, Jain wrote that a computer-science or statistics graduate degree is not strictly necessary and mentioned quantitative backgrounds such as aerospace engineering and economics. That is his practical opinion, not a universal labor-market finding. Degree expectations vary by employer, country, seniority and whether the role is research-oriented.
What the roadmap got right
- It reduces beginner choice overload with a calendar.
- It starts with role and business context rather than algorithms.
- It places quantitative foundations before machine learning.
- It combines theory, projects, visualization and career preparation.
- It treats hiring as part of the learning journey.
Those strengths come from the plan’s structure, not from an independently validated employment study.
What must be added for a credible modern pathway
The visible 2016 list gives limited attention to SQL, relational data modeling, Git, testing, APIs, cloud systems, deployment, monitoring, experimentation, causal reasoning, privacy and fairness. It also emphasizes resource completion more than demonstrated outcomes.
A stronger plan defines an exit artifact after every stage and offers role-specific tracks:
- Analyst or product analytics: SQL, spreadsheets, statistics, experimentation, dashboards and communication.
- Analytics engineering: SQL, data modeling, transformation workflows, testing, documentation and BI.
- Applied machine learning: statistics, modeling, software engineering, deployment and monitoring.
- Research: deeper mathematics, experimental design, publications and potentially graduate study.
- Domain-first transition: apply data skills inside finance, marketing, healthcare, operations or public policy.
A portfolio that demonstrates ability
Instead of collecting certificates, build a balanced set of artifacts:
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- An exploratory analysis with documented cleaning and assumptions.
- A statistical or experimental analysis that explains uncertainty.
- A predictive-modeling project with a defensible validation design.
- A dashboard or visualization narrative for a defined audience.
- An original domain project tied to a real decision or user need.
- An optional competition project presented with limitations rather than only its rank.
Each repository or report should include a clear README, data provenance, methods, reproducibility instructions, limitations and an explanation of what should happen next.
Historical resource notes
The original page links to specific 2016-era courses and tools. Interfaces, syllabi, pricing, licensing and availability may have changed, so do not assume that an old link represents a current course or product.
| Resource named in the plan | Historical role | How to interpret it now |
|---|---|---|
| Coursera and Udacity | Statistics and machine-learning courses | Check the current syllabus, access terms and assessment model |
| Khan Academy | Algebra and probability foundations | Useful for review, not a complete job pathway |
| Codecademy and Dataquest | Interactive Python learning | Supplement guided lessons with independent projects |
| swirl and Advanced R | R instruction | Historical recommendations; verify compatibility and current relevance |
| Kaggle and the Titanic dataset | Competition and notebook practice | Useful practice, weak as a sole portfolio |
| Tableau, QlikView and D3.js | Visualization and BI | Learn visual reasoning first; product editions and workflows differ |
Official home pages include Coursera, Udacity, Khan Academy, Codecademy, Dataquest, Kaggle, Tableau and Qlik. Current prices, free-trial terms, certificates and regional availability require separate verification.
Is one year enough?
One year can establish a foundation or support a move into an adjacent entry-level role, particularly for someone who already has quantitative, programming or domain experience and can produce strong evidence of ability. It cannot guarantee the title “data scientist.” The plan’s calendar is a pacing device; it is not a universal workload, credential or hiring standard.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsFor historical context, the original article and its 2017 retrospective are available at Analytics Vidhya’s year-end resource roundup.
The Bottom Line
The 2016 roadmap remains a useful example of curriculum sequencing: orient the learner, teach quantitative foundations, practice coding and modeling, communicate results and prepare for work. Treat it as a historical starting framework, then add SQL, reproducibility, software practices, responsible data use, role-specific projects and realistic hiring expectations before using it as a modern plan.
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