Top Python Libraries for Data Analysis

Pandas: For data manipulation and analysis.

NumPy: For numerical computations and array operations.

Matplotlib: For creating static visualizations.

Seaborn: For statistical data visualization.

SciPy: For advanced mathematical and scientific computations.

Scikit-learn: For machine learning tasks.

Statsmodels: For statistical modeling and hypothesis testing.

Plotly: For interactive visualizations.

OpenPyXL: For working with Excel files.

PySpark: For big data processing.

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Reality check on Data Analytics jobs:

⟶ Most recruiters & employers are open to different backgrounds
⟶ The "essential skills" are usually a mix of hard and soft skills

Desired hard skills:

⟶ Excel - every job needs it
⟶ SQL - data retrieval and manipulation
⟶ Data Visualization - Tableau, Power BI, or Excel (Advanced)
⟶ Python - Basics, Numpy, Pandas, Matplotlib, Seaborn, Scikit-learn, etc

Desired soft skills:

⟶ Communication
⟶ Teamwork & Collaboration
⟶ Problem Solver
⟶ Critical Thinking

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But don't forget to highlight your soft skills in your job application - they're equally important.

In short: Excel + SQL + Data Viz + Python + Communication + Teamwork + Problem Solver + Critical Thinking = Data Analytics

Python for Data Analysis: Must-Know Libraries 👇👇

Python is one of the most powerful tools for Data Analysts, and these libraries will supercharge your data analysis workflow by helping you clean, manipulate, and visualize data efficiently.

🔥 Essential Python Libraries for Data Analysis:

✅ Pandas – The go-to library for data manipulation. It helps in filtering, grouping, merging datasets, handling missing values, and transforming data into a structured format.

📌 Example: Loading a CSV file and displaying the first 5 rows:

import pandas as pd df = pd.read_csv('data.csv') print(df.head()) 

✅ NumPy – Used for handling numerical data and performing complex calculations. It provides support for multi-dimensional arrays and efficient mathematical operations.

📌 Example: Creating an array and performing basic operations:

import numpy as np arr = np.array([10, 20, 30]) print(arr.mean()) # Calculates the average 

✅ Matplotlib & Seaborn – These are used for creating visualizations like line graphs, bar charts, and scatter plots to understand trends and patterns in data.

📌 Example: Creating a basic bar chart:

import matplotlib.pyplot as plt plt.bar(['A', 'B', 'C'], [5, 7, 3]) plt.show() 

✅ Scikit-Learn – A must-learn library if you want to apply machine learning techniques like regression, classification, and clustering on your dataset.

✅ OpenPyXL – Helps in automating Excel reports using Python by reading, writing, and modifying Excel files.

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Interview list for Data Analytics Roles

SQL Essentials:
- SELECT statements including WHERE, ORDER BY, GROUP BY, HAVING
- Basic JOINS: INNER, LEFT, RIGHT, FULL
- Aggregate functions: COUNT, SUM, AVG, MAX, MIN
- Subqueries, Common Table Expressions (WITH clause)
- CASE statements, advanced JOIN techniques, and Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK)

Excel Proficiency:
- Cell operations, formulas (SUMIFS, COUNTIFS, AVERAGEIFS, LOOKUPS)
- PivotTables, PivotCharts, Data validation, What-if analysis
- Advanced formulas, Data Model & Power Pivot

Power BI Skills:
- Data modeling (importing data, managing relationships)
- Data transformation with Power Query, DAX for calculated columns/measures
- Creating interactive reports and dashboards, visualizations

Data Warehousing:
-Concepts of OLAP vs. OLTP
-Star and Snowflake schema designs
-ETL processes: Extract, Transform, Load
-Data lake vs. data warehouse

Cloud Computing for Data Analytics:
-Benefits of cloud services (AWS, Azure, Google Cloud)
-Data storage solutions: S3, Azure Blob Storage, Google Cloud Storage
-Cloud-based data analytics tools: BigQuery, Redshift, Snowflake
-Cost management and optimization strategies

Python Programming:
- Basic syntax, control structures, data structures (lists, dictionaries)
- Pandas & NumPy for data manipulation: DataFrames, Series, groupby
-plotting with Matplotlib, Seaborn for visualization

Statistics Fundamentals:
- Mean, Median, Mode, Standard Deviation, Variance
- Probability distributions, Hypothesis Testing, P-values
- Confidence Intervals, Correlation, Simple Linear Regression

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Essential Python Libraries for Data Analytics 😄👇

1. NumPy:
- Efficient numerical operations and array manipulation.

2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).

3. Matplotlib:
- 2D plotting library for creating visualizations.

4. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.

5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.

6. PyTorch:
- Deep learning library, particularly popular for neural network research.

7. Django:
- High-level web framework for building robust, scalable web applications.

8. Flask:
- Lightweight web framework for building smaller web applications and APIs.

9. Requests:
- HTTP library for making HTTP requests.

10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.

As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.

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1. Foundations:
- Learn basic SQL syntax, including SELECT, FROM, WHERE clauses.
- Understand data types, constraints, and the basic structure of a database.

2. Database Design:
- Study database normalization to ensure efficient data organization.
- Learn about primary keys, foreign keys, and relationships between tables.

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- Practice writing simple to complex SELECT queries.
- Master different types of joins (INNER, LEFT, RIGHT, FULL) to combine data from multiple tables.

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- Learn optimization techniques, such as avoiding SELECT * and using appropriate indexes.

7. Transactions and ACID Properties:
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- Explore advanced SQL concepts like window functions, CTEs (Common Table Expressions), and recursive queries.
- Familiarize yourself with database-specific features (e.g., PostgreSQL's JSON functions, MySQL's spatial data types).

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