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Data Analytics Courses

Data Analysist

  • April 4, 2025
  • Com 0

1. Introduction to Data Analytics

A Data Analyst uses tools like R, Tableau and Power BI to:

  • Collect & Clean data for accuracy.
  • Analyze & Interpret trends and patterns.
  • Visualize & Present insights using dashboards and reports.

   1.1  Essential Tools & Setup
   1.1.1  Install R & Required Packages

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install.packages(c("tidyverse", "data.table", "ggplot2", "dplyr", "readr"))
library(tidyverse)

   1.1.2  Download Tableau & Power BI

2. Data Analysis with R

   2.1  Loading & Exploring Data

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df <- read.csv("data.csv")  # Load CSV file
head(df)  # View first 6 rows
str(df)  # Data structure
summary(df)  # Summary statistics

   2.2  Data Cleaning & Transformation
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df <- df %>% drop_na()  # Remove missing values
df$column <- as.factor(df$column)  # Convert column to categorical
df <- rename(df, new_name = old_name)  # Rename column
df <- mutate(df, new_column = column1 + column2)  # Add new column

    2.3 Filtering & Sorting Data
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df_filtered <- df %>% filter(Age > 30 & Salary > 50000)  # Filter rows
df_sorted <- df %>% arrange(desc(Salary))  # Sort descending

   2.4  Aggregating Data
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df_summary <- df %>%
  group_by(Department) %>%
  summarise(Avg_Salary = mean(Salary, na.rm=TRUE))

3. Data Visualization in R (ggplot2)

   3.1  Bar Chart

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ggplot(df, aes(x=Department, y=Salary)) + geom_bar(stat="identity", fill="blue")

  3.2  Histogram
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ggplot(df, aes(x=Salary)) + geom_histogram(bins=30, fill="green", color="black")

   3.3  Scatter Plot with Trend Line
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ggplot(df, aes(x=Experience, y=Salary)) + geom_point() + geom_smooth(method="lm", col="red")

 4. Tableau for Data Visualization

   4.1  Steps to Create Dashboards in Tableau

  • Connect Data: Import CSV, Excel, SQL Server or Cloud data.
  • Prepare Data: Use “Data Interpreter” to clean messy data.
  • Create Charts:
    • Drag & drop Dimensions (Categories) and Measures (Numerical).
    • Choose visualization types (Bar Chart, Pie Chart, Heatmap, etc.).
    • Build a Dashboard:
      • Combine multiple charts.
      • Use Filters, Parameters, and Actions for interactivity.
  • Publish & Share:
    • Save to Tableau Public or Server.
    • Export as PDF, Image or Web Report.

   4.2  Common Visuals in Tableau

Chart Type When to Use
Bar Chart Compare categories (e.g., Sales by Region)
Line Chart Show trends over time (e.g., Monthly Revenue)
Heatmap Identify patterns (e.g., Customer Engagement by Hour)
Pie Chart Show proportions (e.g., Market Share)
TreeMap Display hierarchical data (e.g., Sales by Product)

   4.3  Calculated Field in Tableau

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IF [Sales] > 5000 THEN "High" ELSE "Low" END

 5. Power BI for Business Intelligence

   5.1  Steps to Build Reports in Power BI

  • Load Data: Connect to Excel, SQL, Cloud, APIs.
  • Transform Data: Use Power Query to clean & reshape.
  • Build Visualizations:
    • Drag fields into Reports View.
    • Select chart types & apply filters.
  • Create Dashboards:
    • Combine multiple reports.
    • Add Slicers, Filters and Drill-Throughs
  • Publish Reports:
    • Share via Power BI Service.
    • Embed in websites or applications.

   5.2  DAX (Data Analysis Expressions) for Calculated Fields

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Total_Sales = SUM(Sales[Revenue])
Avg_Salary = AVERAGE(Employees[Salary])
Sales_Percentage = DIVIDE([Total_Sales], SUM(Sales[Total]), 0)

   5.3  Common Power BI Charts

Chart Type Use Case
Bar Chart Compare categories (e.g., Sales by Region)
Line Chart Show trends over time
Pie Chart Display proportions
Map Visualization Show geographical data
KPI Card Display key metrics (e.g., Total Sales)

 6. Key Differences: Tableau vs Power BI

Feature Tableau Power BI
Ease of Use User-friendly drag & drop Requires Power Query knowledge
Data Sources Supports multiple sources Best for Microsoft ecosystem
Performance Fast for large datasets Slower for complex models
Visualization High customization Limited compared to Tableau
Cost Expensive Affordable (Free Desktop Version)

 7. Data Analyst Best Practices

  • Data Cleaning: Handle missing values, remove duplicates.
  • Data Exploration: Identify patterns & trends before visualization.
  • Use Filters & Slicers: Allow users to interact with reports.
  • Automate Reports: Schedule updates in Power BI & Tableau.
  • Optimize Performance: Reduce data load & improve dashboard speed.

8. Learning Resources

  •  R for Data Science – Book
  • Tableau Public Guide – Tutorial
  • Power BI Training – Microsoft Docs
  • Kaggle Datasets – Explore

Download Elysium Spark Note