My Workflow as a Junior Data Analyst 

1. I understand the business problem

Before touching any dataset, I make sure I fully understand the context and the objective. I clarify:

• what decision needs to be supported

• which KPIs matter

• who the stakeholders are

• what constraints or assumptions exist

This step ensures that my analysis is aligned with real business needs and not just technical exploration.

2. I collect the data

Once the objective is clear, I identify and gather the relevant data sources. This can include:

• CSV or Excel files

• SQL databases

• internal dashboards

• third‑party datasets

• public datasets

I also check data availability, access permissions, and potential limitations.

3. I clean and prepare the data

Data cleaning is often the most time‑consuming part, but it’s essential. I handle:

• missing values

• duplicates

• inconsistent formats (dates, categories, numbers)

• outliers

• structural issues

My goal is to build a clean, reliable dataset that can support accurate analysis.

4. I explore the data (Exploratory Data Analysis)

I use descriptive statistics and visual exploration to understand the dataset. I look for:

• distributions

• correlations

• trends

• anomalies

• patterns that might influence the analysis

This step helps me identify the most relevant variables and refine the direction of the project.

5. I engineer and transform features

To improve the quality of the analysis, I create new variables or transform existing ones. This includes:

• aggregations

• encoding categorical variables

• creating ratios or indicators

• grouping data by time periods

• normalizing or scaling values

Feature engineering often reveals insights that raw data cannot show.

6. I perform the analysis

Depending on the problem, I apply the appropriate analytical methods:

• descriptive statistics

• comparative analysis

• correlation analysis

• hypothesis testing

• segmentation

• trend analysis

My goal is to answer the business questions with clarity and precision.

7. I visualize the insights

I translate the results into clear, actionable visualizations using tools like:

• Tableau

• Python (Matplotlib, Seaborn, Plotly…)

I focus on readability, relevance, and impact. Each chart must tell a story and support decision‑making.

8. I communicate and present recommendations

I summarize the key findings in a structured and accessible way. I highlight:

• what the data reveals

• what actions can be taken

• what risks or limitations exist

• what opportunities the business can leverage

My goal is to make insights understandable for both technical and non‑technical audiences.

9. I iterate and improve

After presenting the results, I gather feedback from stakeholders. I refine:

• the visuals

• the analysis

• the dataset

• the recommendations

Iteration ensures that the final output is accurate, relevant, and aligned with expectations.