Build Data Analyst Portfolio in 1 month

Path 1 (More focus on SQL & then on Python)
👇👇

Week 1: Learn Fundamentals
Days 1-3: Start with online courses or tutorials on basic data analysis concepts.
Days 4-7: Dive into SQL basics for data retrieval and manipulation.
Free Resources: https://t.iss.one/sqlanalyst/74

Week 2: Data Analysis Projects
Days 8-14: Begin working on simple data analysis projects using SQL. Analyze the data and document your findings.

Week 3: Intermediate Skills
Days 15-21: Start learning Python for data analysis. Focus on libraries like Pandas for data manipulation.
Days 22-23: Explore more advanced SQL topics.

Week 4: Portfolio Completion
Days 24-28: Continue working on your SQL-based projects, applying what you've learned.
Day 29: Transition to Python for your personal project, applying Python's data analysis capabilities.
Day 30: Create a portfolio website showcasing your projects in SQL and Python, along with explanations and code.

Hope it helps :)

Python Libraries For Data Science

Scrap Image from bing using BeautifulSoup

import requests
from bs4 import BeautifulSoup as BSP

def split_url(url):
    return url.split('&')[0]

def get_image_urls(search_query):
    url = f"https://cn.bing.com/images/search?q={search_query}&first=1&cw=1177&ch=678"
    headers = {
        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3"
    }
    rss = requests.get(url, headers=headers)
    soup = BSP(rss.content, "html.parser")

    all_img = []
    for img in soup.find_all('img'):
        img_url = img.get('src2')
        if img_url and img_url.startswith('https://tse2.mm.bing.net/'):
            img_url = split_url(img_url)
            all_img.append(img_url)

    return all_img

print(get_image_urls("cat"))

sample response :

['https://tse2.mm.bing.net/th?q=Cat+Portrait', ...']

Most Important Python Topics for Data Analyst Interview:

#Basics of Python:

1. Data Types

2. Lists

3. Dictionaries

4. Control Structures:

- if-elif-else

- Loops

5. Functions

6. Practice basic FAQs questions, below mentioned are few examples:

- How to reverse a string in Python?

- How to find the largest/smallest number in a list?

- How to remove duplicates from a list?

- How to count the occurrences of each element in a list?

- How to check if a string is a palindrome?

#Pandas:

1. Pandas Data Structures (Series, DataFrame)

2. Creating and Manipulating DataFrames

3. Filtering and Selecting Data

4. Grouping and Aggregating Data

5. Handling Missing Values

6. Merging and Joining DataFrames

7. Adding and Removing Columns

8. Exploratory Data Analysis (EDA):

- Descriptive Statistics

- Data Visualization with Pandas (Line Plots, Bar Plots, Histograms)

- Correlation and Covariance

- Handling Duplicates

- Data Transformation

#Numpy:

1. NumPy Arrays

2. Array Operations:

- Creating Arrays

- Slicing and Indexing

- Arithmetic Operations

#Integration with Other Libraries:

1. Basic Data Visualization with Pandas (Line Plots, Bar Plots)

#Key Concepts to Revise:

1. Data Manipulation with Pandas and NumPy

2. Data Cleaning Techniques

3. File Handling (reading and writing CSV files, JSON files)

4. Handling Missing and Duplicate Values

5. Data Transformation (scaling, normalization)

6. Data Aggregation and Group Operations

7. Combining and Merging Datasets