{{whyLabel}}: Data analytics is highly domain-specific; focusing on a niche like Finance, Healthcare, or E-commerce makes you a more attractive specialist.

{{howLabel}}:

Evaluate your past experience or interests (e.g., if you like retail, focus on Supply Chain Analytics).
Identify 3 key metrics for that industry (e.g., Churn Rate for SaaS, Inventory Turnover for Retail).
Search job boards for 'Data Analyst + [Your Niche]' to see specific domain requirements.

{{doneWhenLabel}}: You have selected one primary industry to tailor your projects and learning towards.

{{whyLabel}}: You don't need to be a math wizard, but understanding the logic behind the data is crucial for accurate interpretation.

{{howLabel}}:

Review basic descriptive statistics: Mean, Median, Mode, and Standard Deviation.
Understand the concept of 'Probability Distributions' and 'Hypothesis Testing'.
Take a 15-minute online self-assessment on basic algebra and logic.

{{doneWhenLabel}}: You have identified specific math gaps to address during your technical preparation.

{{whyLabel}}: Consistency is the most important factor in a career transition; sporadic learning leads to knowledge loss.

{{howLabel}}:

Block 2 hours every weekday evening or 5 hours on Saturday/Sunday.
Use a 'Deep Work' approach: no phone, no social media during these blocks.
Focus on one tool at a time (e.g., SQL for 4 weeks, then Python for 8 weeks).

{{doneWhenLabel}}: Your calendar shows recurring 'Data Study' blocks for the next 3 months.

{{whyLabel}}: Excel is still the most used tool for quick analysis and is often the first step in any data workflow.

{{howLabel}}:

Learn Pivot Tables for rapid data summarization.
Master VLOOKUP and XLOOKUP for merging datasets.
Use 'Power Query' to automate the cleaning of messy CSV files.

{{doneWhenLabel}}: You can clean a raw dataset of 1,000+ rows and create a summary report in under 20 minutes.

{{whyLabel}}: SQL is the industry standard for retrieving data from databases; you cannot be a data analyst without it.

{{howLabel}}:

Download and install PostgreSQL (Open Source).
Practice 'INNER JOIN', 'LEFT JOIN', and 'GROUP BY' clauses using a sample database like 'DVD Rental'.
Learn 'Common Table Expressions' (CTEs) to write readable, complex queries.

{{doneWhenLabel}}: You can write a query that joins three tables and calculates a weighted average.

{{whyLabel}}: This book explains complex statistical concepts using real-world humor and intuition, which is vital for explaining insights to stakeholders.

{{howLabel}}:

Focus on the chapters regarding 'Correlation vs. Causation' and 'The Central Limit Theorem'.
Take notes on how the author explains technical terms to non-technical people.
Aim to read 30 pages per hour.

{{doneWhenLabel}}: You have finished the book and summarized the top 5 statistical pitfalls to avoid.

{{whyLabel}}: Python is the powerhouse for automation and advanced analytics; Pandas is the specific library for data manipulation.

{{howLabel}}:

Install the Anaconda Distribution to get Jupyter Notebooks and Python pre-configured.
Learn to load data using pd.read_csv() and inspect it with .head() and .info().
Practice filtering dataframes and handling missing values with .fillna().

{{doneWhenLabel}}: You can load a dataset in Python and perform a basic group-by analysis.

{{whyLabel}}: Visualization is how you communicate value; Tableau Public is a free, industry-leading tool for showcasing your work.

{{howLabel}}:

Connect to a simple dataset (e.g., global temperatures or sales data).
Create 3 distinct charts: a Line Chart for trends, a Bar Chart for comparisons, and a Map for geographic data.
Combine them into an interactive dashboard with filters.

{{doneWhenLabel}}: You have a live, shareable link to your first interactive dashboard.

{{whyLabel}}: Generic datasets like 'Titanic' or 'Iris' are overused; unique datasets show initiative and industry interest.

{{howLabel}}:

Search Kaggle for datasets related to your chosen niche (e.g., 'E-commerce sales 2024').
Ensure the dataset has at least 5,000 rows and multiple columns for meaningful analysis.
Download the raw CSV and save it as your 'Source' file.

{{doneWhenLabel}}: You have a unique dataset saved and a clear question you want to answer with it.

{{whyLabel}}: 80% of an analyst's job is cleaning data; showing you can handle 'dirty' data is highly valued by hiring managers.

{{howLabel}}:

Use Python or SQL to identify duplicates and null values.
Standardize date formats and categorical labels (e.g., changing 'USA' and 'United States' to one format).
Document every step you took in a 'README' file.

{{doneWhenLabel}}: You have a 'clean' version of your dataset and a script that documents the process.

{{whyLabel}}: EDA is where you find the 'story' in the data, identifying trends, outliers, and correlations.

{{howLabel}}:

Create histograms to see the distribution of your key metrics.
Use scatter plots to find correlations between variables (e.g., Price vs. Sales Volume).
Write down 3 surprising insights you found during the exploration.

{{doneWhenLabel}}: You have a notebook full of visualizations and a list of 3 key business insights.

{{whyLabel}}: GitHub is your technical resume; it proves you can write clean, version-controlled code.

{{howLabel}}:

Create a new repository named '[Niche]-Data-Analysis'.
Upload your Jupyter Notebooks and the cleaned dataset.
Write a professional README that explains the Problem, the Tools used, and the Results.

{{doneWhenLabel}}: Your GitHub profile has at least one complete, well-documented project repository.

{{whyLabel}}: Recruiters use automated tools to find candidates; specific keywords ensure you appear in their searches.

{{howLabel}}:

Add 'Data Analyst' and your niche to your headline.
List 'SQL', 'Python', 'Tableau', and 'Data Visualization' in your skills section.
Feature your GitHub project link in the 'Featured' section of your profile.

{{doneWhenLabel}}: Your profile is 'All-Star' rated and contains at least 10 relevant data keywords.

{{whyLabel}}: Informational interviews provide 'insider' knowledge about the daily role and can lead to referrals.

{{howLabel}}:

Find a Senior Data Analyst on LinkedIn in your target industry.
Send a polite message: 'I admire your work at [Company]. Could I have 15 mins for a virtual coffee to ask about your journey?'
Set a specific calendar invite for next Friday at 2:00 PM.

{{doneWhenLabel}}: You have a confirmed 15-minute meeting on your calendar.

{{whyLabel}}: You will never feel 100% ready; applying is the only way to get real-world feedback on your profile.

{{howLabel}}:

Look for 'Junior Data Analyst' or 'Associate Analyst' roles.
Tailor your resume to mention the specific tools listed in the job description.
Include the link to your GitHub portfolio in the application.

{{doneWhenLabel}}: You have submitted 3 applications and tracked them in a spreadsheet.

{{whyLabel}}: Behavioral interviews are standard; the STAR (Situation, Task, Action, Result) method ensures your answers are structured and impactful.

{{howLabel}}:

Prepare 3 stories: one about a technical challenge, one about a conflict, and one about a successful project.
Quantify your results (e.g., 'Reduced data processing time by 20%').
Practice speaking these stories aloud to a timer.

{{doneWhenLabel}}: You have 3 written STAR stories ready for your first interview.

{{whyLabel}}: Staying updated with 2025/2026 trends (like AI-driven analytics) requires being part of a peer group.

{{howLabel}}:

Join the 'Locally Optimistic' or 'DataTalks.Club' Slack communities.
Introduce yourself and share your GitHub project for feedback.
Attend one virtual 'Meetup' or webinar this month.

{{doneWhenLabel}}: You have participated in at least one discussion or received feedback on your work.

Data analytics career path

Projekt-Plan