“Data is the new oil.” You’ve probably heard this saying a thousand times. It is true that data is driving decision-making across industries, and one of the most sought-after skills in today’s workforce is the ability to analyze data. Regardless of your background student, job seeker, career changer, or simply curious learning data analytics in 30 days is not only possible, but also a smart investment in your future.
In this blog post, we’ll give you a systematic, achievable 30-day plan to start using data analytics. Even if you are a novice with no technical experience, you will leave with solid knowledge and useful skills. Let’s begin.
Why Study Analytics?
Before we begin, let’s understand why data analytics is such a hot skill right now:
- High demand across industries: Data analysts are needed in every industry, including marketing, finance, healthcare, and technology.
- Outstanding pay: In India, entry-level data analysts can make between ₹5 and 10 LPA, while in the US, they can make between $80k and $120k annually.
- Opportunities for remote work: A lot of businesses employ remote data analysts, which allows you flexibility.
- Gateway to data science: If you want to go into machine learning or artificial intelligence, analytics is the first step.
- Increase career growth of 70% up to 2030 .
What Will You Learn in 30 Days?
By the end of this 30-day plan, you’ll have a good understanding of Data Analytics and hands on experience in projects, Industry Ready Projects.
Let’s Begin :
- Excel for data analysis
- SQL for working with databases
- Data visualization tools like Power BI or Tableau
- Basic statistics and business metrics
- Python for data analysis (optional but recommended)
To Understand topics and practical knowledge we have to divide 30 days learning into 4 Weeks with different learning strategies.
Week 1: Understanding the Basics + Excel
Goals:
- Understand what data analytics is ,how data analytics is help full to our day to day business strategies.
- Learn how to work with Excel or Google Sheets.
Topics to Cover:
Types of analytics:
1.Descriptive Analytics:
- What does it describe : Describes what has happened.
- Purpose of this Analytics : Provides historical insights by summarizing past data.
- Basic Example : Sales reports, website traffic reports, or average monthly revenue.
- Practical Tools used : Dashboards, charts, graphs, basic statistics.
2.Diagnostic Analytics:
- What does it describe :Explains why something happened.
- Purpose of this Analytics :Identifies causes and relationships.
- Basic Example : Analyzing why customer sales increased last quarter.
- Practical Tools used : Drill downs, data mining, correlation analysis.
3.Predictive Analytics:
- What does it describe :Predicts what could happen in the future.
- Purpose of this Analytics : Uses historical data to forecast future trends.
- Basic Example : Forecasting next quarter’s sales based on previous patterns.
- Practical Tools used : Machine learning, regression models, forecasting algorithms.
4.Prescriptive Analytics:
- What does it describe : Recommends actions to be take.
- Purpose of this Analytics : Suggests the best course of action based on data predictions.
- Basic Example : Recommending optimal inventory levels or pricing strategies.
- Practical Tools used : Optimization algorithms, simulation, AI-based decision systems.
Goals to learn Data Analyst:
- Learn how to analyze large data sets with Python
- Libraries to Learn:
- Pandas (Pandas is for data manipulation)
- NumPy (NumPy is for numerical data and Satisfaction)
- Matplotlib/Seaborn (Matplotlib or seaborn is used for visualization purpose)
RESOURCES:
There are many resources available to Learn Data Analytics few of them are free and paid ,You can use any of them based on your financial Conditions.
- Google Colab : (free) for coding practice in browser which free of resources.
- Kaggle: In Kaggle we can extract Data in Excel, SQL and also Learn “Python” and “Pandas” free courses
- YouTube: “Python for Data Analysis” by Corey Schafer
Tools Used
- Excel or Google Sheets
- SQL (via MySQL, SQLite, or PostgreSQL)
- Power BI or Tableau Public for visualization.
- Python (Jupyter Notebook / Google Colab)
- Kaggle for datasets and competitions with based project we can extract.
- GitHub for project hosting.