Skills Required for Data Analyst

Data analysts must be proficient in programming languages for data manipulation and analysis. The most commonly used languages include:

• Python – Used for data analysis, visualization, and machine learning.

• R – Popular for statistical analysis and data science applications.

• SQL – Essential for querying databases and extracting relevant data.

1. Variables & Data Types

Python Example:

# Integer
age = 25  
# Float
height = 5.9  
# String
name = "Alice"
# Boolean
is_student = True

print("Name:", name)
print("Age:", age)
print("Height:", height)
print("Is Student:", is_student)

Output:

Name: Alice
Age: 25
Height: 5.9
Is Student: True

2. Operators

Python Example:

# Arithmetic Operators
a = 10
b = 3
print("Addition:", a + b)  # 10 + 3
print("Subtraction:", a - b)  # 10 - 3
print("Multiplication:", a * b)  # 10 * 3
print("Division:", a / b)  # 10 / 3

Output:

Addition: 13
Subtraction: 7
Multiplication: 30
Division: 3.3333

3. Control Flow

Python Example:

# If-else Statements
age = 18
if age >= 18:
    print("You are an adult.")
else:
    print("You are a minor.")

Output:

You are an adult.

4. Functions

Python Example:

def greet(name, age):
    print(f"Hello, {name}! You are {age} years old.")

greet("Alice", 25)
greet("Bob", 30)

Output:

Hello, Alice! You are 25 years old.
Hello, Bob! You are 30 years old.

SQL Basics

1. Basic SELECT Query

SELECT * FROM Employees;

2. SELECT with Specific Columns

SELECT first_name, last_name, salary FROM Employees;

3. WHERE Clause (Filtering Data)

SELECT * FROM Employees WHERE salary > 50000;

Key Skills Required for a Data Analyst

To excel in data analytics, mastering the skills required for a data analyst is crucial. These include:

• Technical Skills: Proficiency in Python, R, and SQL.

• Data Visualization: Knowledge of tools like Tableau, Power BI, or Matplotlib.

• Statistics & Mathematics: Understanding probability, regression, and hypothesis testing.

• Business Acumen: Ability to interpret data in a business context.

• Communication Skills: Presenting insights clearly and concisely.

• Problem-Solving Skills: Identifying

Explanation: These queries show how to use AND, OR, and NOT to filter results.

5. ORDER BY (Sorting Data)

Use ORDER BY to sort data by one or more columns:

— Select all employees, sorted by salary in descending order

SELECT * FROM Employees

ORDER BY salary DESC;

— Select all employees, sorted by department in ascending order

SELECT * FROM Employees

ORDER BY department ASC;

Explanation: The ORDER BY clause sorts the results by the specified column. Use ASC for ascending and DESC for descending order.

6. LIMIT (Limiting the Number of Results)

Use LIMIT to restrict the number of rows returned:

— Select the first 5 employees

SELECT * FROM Employees

LIMIT 5;

Explanation: This will return the first 5 rows from the Employees table.

7. JOIN (Combining Data from Multiple Tables)

Use JOIN to combine rows from two or more tables based on a related column:

— INNER JOIN: Select employees and their department names

SELECT Employees.first_name, Employees.last_name, Departments.department_name

FROM Employees

INNER JOIN Departments

ON Employees.department_id = Departments.department_id;

Explanation: This will return a list of employees along with their respective department names by matching department_id from both tables.

8. GROUP BY (Grouping Data)

Use GROUP BY to group rows that share a property:

— Select the average salary in each department

SELECT department, AVG(salary) AS avg_salary

FROM Employees

GROUP BY department;

Explanation: This query groups employees by department and calculates the average salary in each department.

9. HAVING Clause (Filtering Groups)

Use HAVING to filter groups after GROUP BY is applied:

— Select departments with an average salary greater than 60,000

SELECT department, AVG(salary) AS avg_salary

FROM Employees

GROUP BY department

HAVING AVG(salary) > 60000;

Explanation: The HAVING clause is used to filter the result of GROUP BY, unlike WHERE, which filters rows before grouping.

10. INSERT INTO (Inserting Data)

Insert new records into a table:

— Insert a new employee record

INSERT INTO Employees (first_name, last_name, department, salary)

VALUES (‘John’, ‘Doe’, ‘Marketing’, 55000);

Explanation: This will insert a new row into the Employees table with the specified values.

11. UPDATE (Modifying Data)

Use UPDATE to modify existing records:

— Update the salary of an employee

UPDATE Employees

SET salary = 60000

WHERE employee_id = 1;

Explanation: This query updates the salary of the employee with employee_id 1 to 60,000.

12. DELETE (Removing Data)

Use DELETE to remove records:

— Delete an employee record

DELETE FROM Employees

WHERE employee_id = 1;

Explanation: This query deletes the employee with employee_id 1 from the Employees table.

13. Subquery (Nested Queries)

Use a subquery to perform operations within another query:

— Select employees who earn more than the average salary

SELECT * FROM Employees

WHERE salary > (SELECT AVG(salary) FROM Employees);

Explanation: This query uses a subquery to calculate the average salary and return employees who earn more than the average.

14. ALTER TABLE (Modifying Table Structure)

Use ALTER TABLE to modify an existing table structure:

— Add a new column to the Employees table

ALTER TABLE Employees

ADD date_of_birth DATE;

Explanation: This query adds a new column called date_of_birth to the Employees table.

15. DISTINCT (Removing Duplicates)

Use DISTINCT to return only unique values:

— Select distinct departments

SELECT DISTINCT department

FROM Employees;

Explanation: This query returns a list of unique departments from the Employees table.

Summary:

1. Basic SELECT Query1. Basic SELECT Query1. Basic SELECT Query

• • SELECT is used to query data.
  • WHERE filters data based on conditions.
  • ORDER BY sorts data.
  • LIMIT restricts the number of rows returned.
  • JOIN combines data from multiple tables.
  • GROUP BY and HAVING help with aggregations.
  • INSERT, UPDATE, and DELETE are for modifying data.
  • Subqueries allow for complex queries.
  • DISTINCT removes duplicate values.