DATA ANALYTICS

Data analytics is the art and science of drawing actionable insights from data.

Data Analytics + Business Knowledge = Impact/Value Creation for the Business

Overall, if you understand your business administration and have the capability to perform exploratory data analysis to gather the required information, then you are good to go with a career in data analytics.

When I started learning data analytics, I literally didn’t know anything. So, I am creating this blog for students like me. I also welcome professionals and seniors to correct me if I miss anything or write something incorrectly.

EDA (Exploratory Data Analysis) plays a crucial role in data analytics—like tea without sugar, it’s essential! ??

1. Data Cleaning: Missing values, duplicates, and data formatting ko theek karna.

2. Data Visualization: Graphs (like histograms, scatter plots, bar charts) ke zariye patterns aur trends ko samajhna.

3. Statistical Analysis: Basic statistics (mean, median, mode, variance) calculate karna.

4. Correlation Analysis: Variables ke beech relationship ko check karna.

5. Outliers Detection: Unusual data points ko identify karna jo analysis ko impact kar sakte hain.

In short, EDA ka maksad data ko samajhna aur insights nikalna hota hai.

Why Do We Need Data Analytics?

- Helps businesses monitor, manage, and collect performance measures to improve decision-making.

Improves business operations.

Enhances consumer engagement, corporate performance, and boosts revenue.

Enables decisions based on verifiable, data-driven proofs.

Data Analytics Benefits Enterprises by:

Gathering Hidden Insights: Revealing valuable insights from data that align with business needs.

Generating Reports: Providing reports to teams for further actions to boost business and gain a competitive edge.

Performing Market Analysis: Understanding market sentiments, competitors' strengths, and weaknesses.

- Improving Business Requirements: Enhancing business operations to meet consumer expectations and requirements.

Essential Skills to Become a Data Analyst:

Knowledge of mathematical statistics.

Proficiency in R and Python.

Data wrangling skills(data wrangling is crucial for preparing data so that it can be effectively analyzed and used to gain insights. It involves a series of steps to clean, transform, and organize data, ensuring it is accurate, complete, and formatted correctly.)

Skills in Statistics, Data Cleaning, Exploratory Data Analysis, and Data Visualization.

Familiarity with Machine Learning to stand out.

Proficiency in Structured Query Language (SQL).

Statistical visualization and critical thinking.

- Extensive knowledge in Microsoft Excel.

How Does a Data Analyst Work?

Translates numbers into plain English.

Collects, cleans, and interprets data sets to answer questions or solve problems.

Presents findings in comprehensive reports.

Updated List with SQL Included:

Tools Used in Data Analytics:

1. R Programming: For statistical computing and data visualization.

2. Python: With libraries like Pandas and Matplotlib for data analysis.

3. Tableau Public: For creating interactive visualizations and dashboards.

4. Microsoft Excel: For summarizing data and creating pivot tables.

5. SQL: For managing and querying relational databases.

Types of Data Analytics:

1. Predictive Analytics: Identifies trends and forecasts future outcomes.

2. Descriptive Analytics: Provides summaries and reports on historical data.

Why Study Data Analytics?

Studying data analytics helps in making data-driven decisions, understanding trends, and improving business strategies.

Data-Driven ka matlab hai, decisions aur strategies ko data aur facts ke basis par banana, na ki intuition ya guesswork par.

Example: Ek company jo data-driven approach follow karti hai, woh sales data ka analysis karke customer preferences aur buying patterns samajh kar marketing strategies design karti hai, jisse sales aur customer satisfaction improve hoti hai.

Data Visualization for Decision Making

- Definition: Data visualization refers to presenting data graphically through charts, graphs, and maps, making complex data easier to understand and analyze.

- Importance: It helps in spotting trends, patterns, and outliers in data, enabling better insights and data-driven decisions.

- Tools and Technologies: Essential for handling large datasets. Examples include Plotly, DataHero, Tableau, Dygraphs, QlikView, and ZingChart.

- Types of Visualizations:

- Charts: Display data visually (e.g., bar charts, pie charts).

- Tables: Organize data in rows and columns.

- Graphs: Show relationships between variables (e.g., line graphs, scatter plots).

- Maps: Visualize geographic data.

- Dashboards: Combine visualizations into a single view.

- Applications: Key for big data processing, creating service management dashboards, and supporting analysis and design in various sectors.

Data visualization turns raw data into actionable insights, making it crucial in the data analytics process.

Final Note: If I missed anything, please correct me. We have a lot to learn in this journey, and learning should never stop—life is a continuous learning process. Let’s keep pushing forward!

Feel free to drop any questions or doubts in the comments below! I'm here to help and learn together.

#DataAnalytics #DataVisualization #DataDriven #ContinuousLearning

---