Starting your journey as a data analyst is an amazing start for your career. As you progress, you might find new areas that pique your interest:

• Data Science: If you enjoy diving deep into statistics, predictive modeling, and machine learning, this could be your next challenge.

• Data Engineering: If building and optimizing data pipelines excites you, this might be the path for you.

• Business Analysis: If you're passionate about translating data into strategic business insights, consider transitioning to a business analyst role.

But remember, even if you stick with data analysis, there's always room for growth, especially with the evolving landscape of AI.

No matter where your path leads, the key is to start now.

𝗜𝗺𝗽𝗿𝗲𝘀𝘀 𝗥𝗲𝗰𝗿𝘂𝗶𝘁𝗲𝗿𝘀 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝟱 𝗦𝗤𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀!😍

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Pandas is a popular Python library for data manipulation and analysis. Here are some essential concepts in Pandas that every data analyst should be familiar with:

1. Data Structures: Pandas provides two main data structures: Series and DataFrame. A Series is a one-dimensional array-like object, while a DataFrame is a two-dimensional tabular data structure similar to a spreadsheet.

2. Indexing and Selection: Pandas allows you to select and manipulate data using various indexing techniques, such as label-based indexing (loc), integer-based indexing (iloc), and boolean indexing.

3. Data Cleaning: Pandas provides functions for handling missing data, removing duplicates, and filling in missing values. Methods like dropna(), fillna(), and drop_duplicates() are commonly used for data cleaning.

4. Data Manipulation: Pandas offers powerful tools for data manipulation, such as merging, joining, concatenating, reshaping, and grouping data. Functions like merge(), concat(), pivot_table(), and groupby() are commonly used for data manipulation tasks.

5. Data Aggregation: Pandas allows you to aggregate data using functions like sum(), mean(), count(), min(), max(), and custom aggregation functions. These functions help summarize and analyze data at different levels.

6. Time Series Analysis: Pandas has built-in support for working with time series data, including date/time indexing, resampling, shifting, rolling window calculations, and time zone handling.

7. Data Visualization: Pandas integrates well with popular data visualization libraries like Matplotlib and Seaborn to create visualizations directly from DataFrames. You can plot data using functions like plot(), hist(), scatter(), and boxplot().

8. Handling Categorical Data: Pandas provides support for working with categorical data through the Categorical data type. This helps in efficient storage and analysis of categorical variables.

9. Reading and Writing Data: Pandas can read data from various file formats such as CSV, Excel, SQL databases, JSON, and HTML. It can also write data back to these formats after processing.

10. Performance Optimization: Pandas offers methods to optimize performance, such as vectorized operations (using NumPy arrays), using apply() function efficiently, and avoiding loops for faster data processing.

By mastering these essential concepts in Pandas, you can efficiently manipulate and analyze data, perform complex operations, and derive valuable insights from your datasets as a data analyst. Regular practice and hands-on experience with Pandas will further enhance your skills in data manipulation and analysis.

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5 misconceptions about data analytics (and what's actually true):

❌ The more sophisticated the tool, the better the analyst
✅ Many analysts do their jobs with "basic" tools like Excel

❌ You're just there to crunch the numbers
✅ You need to be able to tell a story with the data

❌ You need super advanced math skills
✅ Understanding basic math and statistics is a good place to start

❌ Data is always clean and accurate
✅ Data is never clean and 100% accurate (without lots of prep work)

❌ You'll work in isolation and not talk to anyone
✅ Communication with your team and your stakeholders is essential

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30-day roadmap to learn Python up to an intermediate level

Week 1: Python Basics
*Day 1-2:*
- Learn about Python, its syntax, and how to install Python on your computer.
- Write your first "Hello, World!" program.
- Understand variables and data types (integers, floats, strings).

*Day 3-4:*
- Explore basic operations (arithmetic, string concatenation).
- Learn about user input and how to use the input() function.
- Practice creating and using variables.

*Day 5-7:*
- Dive into control flow with if statements, else statements, and loops (for and while).
- Work on simple programs that involve conditions and loops.

Week 2: Functions and Modules
*Day 8-9:*
- Study functions and how to define your own functions using def.
- Learn about function arguments and return values.

*Day 10-12:*
- Explore built-in functions and libraries (e.g., len(), random, math).
- Understand how to import modules and use their functions.

*Day 13-14:*
- Practice writing functions for common tasks.
- Create a small project that utilizes functions and modules.

Week 3: Data Structures
*Day 15-17:*
- Learn about lists and their operations (slicing, appending, removing).
- Understand how to work with lists of different data types.

*Day 18-19:*
- Study dictionaries and their key-value pairs.
- Practice manipulating dictionary data.

*Day 20-21:*
- Explore tuples and sets.
- Understand when and how to use each data structure.

Week 4: Intermediate Topics
*Day 22-23:*
- Study file handling and how to read/write files in Python.
- Work on projects involving file operations.

*Day 24-26:*
- Learn about exceptions and error handling.
- Explore object-oriented programming (classes and objects).

*Day 27-28:*
- Dive into more advanced topics like list comprehensions and generators.
- Study Python's built-in libraries for web development (e.g., requests).

*Day 29-30:*
- Explore additional libraries and frameworks relevant to your interests (e.g., NumPy for data analysis, Flask for web development, or Pygame for game development).
- Work on a more complex project that combines your knowledge from the past weeks.

Throughout the 30 days, practice coding daily, and don't hesitate to explore Python's documentation and online resources for additional help. Learning Python is a dynamic process, so adapt the roadmap based on your progress and interests.

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Goldman Sachs senior data analyst interview asked questions

SQL

1 find avg of salaries department wise from table
2 Write a SQL query to see employee name and manager name using a self-join on 'employees' table with columns 'emp_id', 'name', and 'manager_id'.
3 newest joinee for every department (solved using lead lag)

POWER BI

1. What does Filter context in DAX mean?
2. Explain how to implement Row-Level Security (RLS) in Power BI.
3. Describe different types of filters in Power BI.
4. Explain the difference between 'ALL' and 'ALLSELECTED' in DAX.
5. How do you calculate the total sales for a specific product using DAX?

PYTHON

1. Create a dictionary, add elements to it, modify an element, and then print the dictionary in alphabetical order of keys.
2. Find unique values in a list of assorted numbers and print the count of how many times each value is repeated.
3. Find and print duplicate values in a list of assorted numbers, along with the number of times each value is repeated.

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Python in a nutshell

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Here are 5 key Python libraries/ concepts that are particularly important for data analysts:

1. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data. Pandas offers functions for reading and writing data, cleaning and transforming data, and performing data analysis tasks like filtering, grouping, and aggregating.

2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. NumPy is often used in conjunction with Pandas for numerical computations and data manipulation.

3. Matplotlib and Seaborn: Matplotlib is a popular plotting library in Python that allows you to create a wide variety of static, interactive, and animated visualizations. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive and informative statistical graphics. These libraries are essential for data visualization in data analysis projects.

4. Scikit-learn: Scikit-learn is a machine learning library in Python that provides simple and efficient tools for data mining and data analysis tasks. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more. Scikit-learn also offers tools for model evaluation, hyperparameter tuning, and model selection.

5. Data Cleaning and Preprocessing: Data cleaning and preprocessing are crucial steps in any data analysis project. Python offers libraries like Pandas and NumPy for handling missing values, removing duplicates, standardizing data types, scaling numerical features, encoding categorical variables, and more. Understanding how to clean and preprocess data effectively is essential for accurate analysis and modeling.

By mastering these Python concepts and libraries, data analysts can efficiently manipulate and analyze data, create insightful visualizations, apply machine learning techniques, and derive valuable insights from their datasets.

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Python Interview Questions for data analyst interview

Question 1: Find the top 5 dates when the percentage change in Company A's stock price was the highest.

Question 2: Calculate the annualized volatility of Company B's stock price. (Hint: Annualized volatility is the standard deviation of daily returns multiplied by the square root of the number of trading days in a year.)

Question 3: Identify the longest streaks of consecutive days when the stock price of Company A was either increasing or decreasing continuously.

Question 4: Create a new column that represents the cumulative returns of Company A's stock price over the year.

Question 5: Calculate the 7-day rolling average of both Company A's and Company B's stock prices and find the date when the two rolling averages were closest to each other.

Question 6: Create a new DataFrame that contains only the dates when Company A's stock price was above its 50-day moving average, and Company B's stock price was below its 50-day moving average