Essential Jupyter Notebook Shortcut Keys

Mode Switching:

Enter: Switch to Edit Mode (write code/text).

Esc: Switch to Command Mode (navigate and execute commands).


Cell Operations:

A: Insert a new cell above.

B: Insert a new cell below.

D, D: Delete the selected cell.

Z: Undo the last cell deletion.


Run and Execution:

Shift + Enter: Run the current cell and move to the next one.

Ctrl + Enter: Run the current cell without moving.

Alt + Enter: Run the current cell and insert a new one below.


Text Formatting (Markdown):

M: Convert cell to Markdown.

Y: Convert cell to Code.


Navigation:

Up/Down Arrow: Move between cells in Command Mode.

Ctrl + Shift + -: Split the cell at the cursor position.


Other Useful Commands:

Ctrl + S: Save the notebook.

Shift + Tab: View function or method documentation.

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Essential Tableau Shortcut Keys for Data Analysts

Ctrl + N: Create a new workbook.

Ctrl + O: Open an existing workbook.

Ctrl + S: Save the workbook.

F11: Toggle Full Screen Mode.

Ctrl + D: Duplicate the current worksheet.

Ctrl + W: Close the current workbook.

Alt + Shift + D: Toggle the Data Pane.

Alt + Shift + F: Toggle the Analytics Pane.

Ctrl + T: Open the Format Pane.

Ctrl + Shift + B: Show/Hide the Toolbar.

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🌟 Data Analyst vs Business Analyst: Quick comparison 🌟

1. Data Analyst: Dives into data, cleans it up, and finds hidden insights like Sherlock Holmes. 🕵️‍♂️

Business Analyst: Talks to stakeholders, defines requirements, and ensures everyone’s on the same page. The diplomat. 🤝


2. Data Analyst: Master of Excel, SQL, Python, and dashboards. Their life is rows, columns, and code. 📊

Business Analyst: Fluent in meetings, presentations, and documentation. Their life is all about people and processes. 🗂️


3. Data Analyst: Focuses on numbers, patterns, and trends to tell a story with data. 📈

Business Analyst: Focuses on the "why" behind the numbers to help the business make decisions. 💡


4. Data Analyst: Creates beautiful Power BI or Tableau dashboards that wow stakeholders. 🎨

Business Analyst: Uses those dashboards to present actionable insights to the C-suite. 🎤


5. Data Analyst: SQL queries, Python noscripts, and statistical models are their weapons. 🛠️

Business Analyst: Process diagrams, requirement docs, and communication are their superpowers. 🦸‍♂️


6. Data Analyst: “Why is revenue declining? Let me analyze the sales data.”

Business Analyst: “Why is revenue declining? Let’s talk to the sales team and fix the process.”


7. Data Analyst: Works behind the scenes, crunching data and making sense of numbers. 🔢

Business Analyst: Works with teams to ensure that processes, strategies, and technologies align with business goals. 🎯


8. Data Analyst: Uses data to make decisions—raw data is their best friend. 📉

Business Analyst: Uses data to support business decisions and recommends solutions to improve processes. 📝


9. Data Analyst: Aims for accuracy, precision, and statistical significance in every analysis. 🧮

Business Analyst: Aims to understand business needs, optimize workflows, and align solutions with business objectives. 🏢


10. Data Analyst: Focuses on extracting insights from data for current or historical analysis. 🔍

Business Analyst: Looks forward, aligning business strategies with long-term goals and improvements. 🌱

Both roles are vital, but they approach the data world in their unique ways.

Choose your path wisely! 🚀

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Essential Power BI Shortcut Keys for Data Analysts

Ctrl + N: Create a new report.

Ctrl + O: Open an existing report.

Ctrl + S: Save the report.

Ctrl + Z / Ctrl + Y: Undo/Redo actions.

Ctrl + C / Ctrl + V: Copy/Paste visuals or data.

Ctrl + X: Cut selected items.

Ctrl + Shift + L: Open the Filters Pane.

Ctrl + T: Add a new table visual.

Alt + Shift + Arrow Keys: Nudge visuals in small increments.

Ctrl + Shift + F: Toggle between Full-Screen and Normal view.

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If you dream of becoming a data analyst, let 2025 be the year you make it happen.

Work hard, stay focused, and change your life.

Happy New Year! May this year bring you success and new opportunities 💪

Essential Data Visualization Tips for Analysts

Simplify Your Visuals: Avoid overcrowding with too much data.

Use Consistent Colors: Maintain uniformity for better readability.

Leverage Contrast: Highlight key insights using contrast.

Focus on Audience Needs: Tailor visuals for your target audience.

Label Clearly: Use concise and clear labels for charts and graphs.

Avoid Unnecessary 3D Effects: Stick to 2D for accurate representation.

Maintain Alignment: Ensure visuals are properly aligned for a professional look.

Tell a Story: Present insights in a logical flow for better comprehension.

Limit Chart Types: Use the right chart for the right data (e.g., bar, line, scatter).

Validate Data Accuracy: Always double-check your data sources and calculations.

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Advanced SQL Optimization Tips for Data Analysts

Use Proper Indexing: Create indexes for frequently queried columns.

Avoid SELECT *: Specify only required columns to improve performance.

Use WHERE Instead of HAVING: Filter data early in the query.

Limit Joins: Avoid excessive joins to reduce query complexity.

Apply LIMIT or TOP: Retrieve only the required rows.

Optimize Joins: Use INNER JOIN over OUTER JOIN where applicable.

Use Temporary Tables: Break complex queries into smaller parts.

Avoid Functions on Indexed Columns: It prevents index usage.

Use CTEs for Readability: Simplify nested queries using Common Table Expressions.

Analyze Execution Plans: Identify bottlenecks and optimize queries.

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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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Data Visualization Tools & Best Practices

1. Power BI:

Purpose: Powerful business analytics tool to visualize and share insights from your data.

Best Practices:

Use simple visuals (avoid overloading with data).

Choose the right chart type (e.g., bar chart for comparisons, line chart for trends).

Use slicers and filters to allow users to explore data interactively.

Keep your color schemes consistent and avoid too many colors.

Use Tooltips for additional context without cluttering the visual.


2. Tableau:

Purpose: Data visualization tool used for creating interactive and shareable dashboards.

Best Practices:

Minimize clutter by reducing non-essential elements (e.g., gridlines, unnecessary labels).

Ensure readability with a clean and intuitive layout.

Use dual-axis charts when comparing two measures in a single visual.

Keep noscripts and labels concise; avoid redundant information.

Prioritize data integrity (avoid misleading visualizations).


3. Matplotlib & Seaborn (Python):

Purpose: Python libraries for static, animated, and interactive visualizations.

Best Practices:

Use subplots to visualize multiple charts together for comparison.

Keep axes readable with appropriate noscripts and labels.

Choose appropriate color palettes (e.g., Seaborn has good built-in color schemes).

Annotations can help clarify key points on the chart.

Use log scaling for large numerical ranges to make the data more interpretable.


4. Excel:

Purpose: Widely used tool for simple data analysis and visualization.

Best Practices:

Use pivot charts to summarize data interactively.

Stick to basic chart types (e.g., bar, line, pie) for easy-to-understand visuals.

Use conditional formatting to highlight key trends or outliers.

Label charts clearly (noscripts, axis names, and legends).

Limit the number of chart elements (don’t overcrowd your chart).


5. Google Data Studio:

Purpose: Free tool for creating dashboards and reports, often integrated with Google products.

Best Practices:

Link to live data sources for automatic updates (e.g., Google Sheets, Google Analytics).

Use dynamic filters to give users control over what data is shown.

Utilize templates for consistent reports and visuals.

Keep reports simple and focused on key metrics.

Design with mobile responsiveness in mind for accessibility.


6. Best Practices for Data Visualization:

Clarity over complexity: Simplify your visuals, removing unnecessary elements.

Choose the right chart: Select charts that best represent the data (e.g., bar for comparisons, line for trends).

Tell a story: Your visual should communicate a clear message or insight.

Consistency in design: Maintain a consistent style for fonts, colors, and layout across all visuals.

Be mindful of colorblindness: Use color schemes that are accessible to all viewers.

Provide context: Include clear noscripts, labels, and legends for better understanding.

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🌟 Data Cleaning Best Practices 🌟

✅ Remove Duplicates: Ensure data accuracy by eliminating duplicate rows.
✅ Handle Missing Values: Use imputation or remove rows/columns with missing data.
✅ Standardize Formats: Ensure consistency in date, time, and number formats.
✅ Remove Outliers: Identify and handle outliers to improve data quality.
✅ Trim Whitespace: Clean leading or trailing spaces in text fields.
✅ Correct Data Types: Convert columns to appropriate data types (e.g., numbers, dates).
✅ Normalize Data: Scale numerical values to a common range for better analysis.
✅ Use Consistent Naming: Standardize naming conventions for columns and variables.
✅ Check for Inconsistencies: Identify and correct mismatched categories or values.
✅ Validate Data: Cross-check data with original sources to ensure accuracy.

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Key Power BI Functions Every Analyst Should Master

DAX Functions:

1. CALCULATE():

Purpose: Modify context or filter data for calculations.

Example: CALCULATE(SUM(Sales[Amount]), Sales[Region] = "East")



2. SUM():

Purpose: Adds up column values.

Example: SUM(Sales[Amount])



3. AVERAGE():

Purpose: Calculates the mean of column values.

Example: AVERAGE(Sales[Amount])



4. RELATED():

Purpose: Fetch values from a related table.

Example: RELATED(Customers[Name])



5. FILTER():

Purpose: Create a subset of data for calculations.

Example: FILTER(Sales, Sales[Amount] > 100)



6. IF():

Purpose: Apply conditional logic.

Example: IF(Sales[Amount] > 1000, "High", "Low")



7. ALL():

Purpose: Removes filters to calculate totals.

Example: ALL(Sales[Region])



8. DISTINCT():

Purpose: Return unique values in a column.

Example: DISTINCT(Sales[Product])



9. RANKX():

Purpose: Rank values in a column.

Example: RANKX(ALL(Sales[Region]), SUM(Sales[Amount]))



10. FORMAT():

Purpose: Format numbers or dates as text.

Example: FORMAT(TODAY(), "MM/DD/YYYY")

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Power BI DAX Functions Every Analyst Should Know

SUM(): Adds all values in a column.

AVERAGE(): Returns the average of a column.

COUNT(): Counts the number of rows in a column.

IF(): Performs conditional logic (True/False).

CALCULATE(): Modifies the context of a calculation.

FILTER(): Returns a table that represents a subset of another table.

ALL(): Removes filters from a table or column.

RELATED(): Retrieves related values from another table.

DISTINCT(): Returns unique values in a column.

DATEADD(): Shifts dates by a specified number of intervals (days, months, etc.).

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Data Visualization Tools Comparison

Power BI:

Best for: Interactive dashboards and reports.

Strengths: Seamless integration with Microsoft products, strong DAX functions.

Weaknesses: Can be resource-heavy with large datasets.


Tableau:

Best for: Advanced data visualizations and storytelling.

Strengths: User-friendly drag-and-drop interface, powerful visual capabilities.

Weaknesses: Higher cost, steeper learning curve for complex analyses.


Excel:

Best for: Quick data analysis and small-scale visualizations.

Strengths: Widely used, simple to learn, great for quick charts.

Weaknesses: Limited in handling large datasets, fewer customization options.


Google Data Studio:

Best for: Free, cloud-based visualizations.

Strengths: Easy collaboration, integrates well with Google products.

Weaknesses: Fewer advanced features compared to Tableau and Power BI.

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Excel Formulas Every Analyst Should Know

SUM(): Adds a range of numbers.

AVERAGE(): Calculates the average of a range.

VLOOKUP(): Searches for a value in the first column and returns a corresponding value.

HLOOKUP(): Searches for a value in the first row and returns a corresponding value.

INDEX(): Returns the value of a cell in a given range based on row and column numbers.

MATCH(): Finds the position of a value in a range.

IF(): Performs a logical test and returns one value for TRUE, another for FALSE.

COUNTIF(): Counts cells that meet a specific condition.

CONCATENATE(): Joins two or more text strings together.

LEFT()/RIGHT(): Extracts a specified number of characters from the left or right of a text string.

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Machine Learning Basics for Data Analysts

Supervised Learning:

Definition: Models are trained on labeled data (e.g., regression, classification).

Example: Predicting house prices (regression) or classifying emails as spam or not (classification).


Unsupervised Learning:

Definition: Models are trained on unlabeled data to find hidden patterns (e.g., clustering, association).

Example: Grouping customers by purchasing behavior (clustering).


Feature Engineering:

Definition: The process of selecting, modifying, or creating new features from raw data to improve model performance.


Model Evaluation:

Definition: Assess model performance using metrics like accuracy, precision, recall, and F1-score for classification or RMSE for regression.


Cross-Validation:

Definition: Splitting data into multiple subsets to test the model's generalizability and avoid overfitting.


Algorithms:

Common Types: Linear regression, decision trees, k-nearest neighbors, and random forests.

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Essential Tableau Shortcuts for Efficiency

Navigating the View:

Ctrl + Tab: Switch between open Tableau workbooks.

Ctrl + 1: Go to the "Data" pane.

Ctrl + 2: Go to the "Analytics" pane.

Ctrl + 3: Go to the "Sheet" tab.


Workbooks and Sheets:

Ctrl + N: Create a new workbook.

Ctrl + Shift + N: Create a new dashboard.

Ctrl + M: Create a new worksheet.

Ctrl + W: Close the current workbook.


Editing:

Ctrl + Z: Undo the last action.

Ctrl + Y: Redo the last undone action.

Ctrl + C: Copy selected items.

Ctrl + V: Paste copied items.

Ctrl + X: Cut selected items.


Data and Views:

Ctrl + Shift + D: Show or hide the "Data" pane.

Ctrl + Shift + T: Show or hide the "Toolbar".

Ctrl + Shift + F: Toggle full-screen mode.


Filtering and Marking:

Ctrl + Shift + L: Show or hide the "Legend" pane.

Ctrl + Shift + K: Add a filter to the view.

Ctrl + Shift + R: Refresh the data.


Navigation within Worksheets:

Arrow keys: Move between fields in a worksheet.

Ctrl + F: Open the search dialog box.

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Common Data Cleaning Techniques for Data Analysts

Remove Duplicates:

Purpose: Eliminate repeated rows to maintain unique data.

Example: SELECT DISTINCT column_name FROM table;


Handle Missing Values:

Purpose: Fill, remove, or impute missing data.

Example:

Remove: df.dropna() (in Python/Pandas)

Fill: df.fillna(0)


Standardize Data:

Purpose: Convert data to a consistent format (e.g., dates, numbers).

Example: Convert text to lowercase: df['column'] = df['column'].str.lower()


Remove Outliers:

Purpose: Identify and remove extreme values.

Example: df = df[df['column'] < threshold]


Correct Data Types:

Purpose: Ensure columns have the correct data type (e.g., dates as datetime, numeric values as integers).

Example: df['date'] = pd.to_datetime(df['date'])


Normalize Data:

Purpose: Scale numerical data to a standard range (0 to 1).

Example: from sklearn.preprocessing import MinMaxScaler; df['scaled'] = MinMaxScaler().fit_transform(df[['column']])


Data Transformation:

Purpose: Transform or aggregate data for better analysis (e.g., log transformations, aggregating columns).

Example: Apply log transformation: df['log_column'] = np.log(df['column'] + 1)


Handle Categorical Data:

Purpose: Convert categorical data into numerical data using encoding techniques.

Example: df['encoded_column'] = pd.get_dummies(df['category_column'])


Impute Missing Values:

Purpose: Fill missing values with a meaningful value (e.g., mean, median, or a specific value).

Example: df['column'] = df['column'].fillna(df['column'].mean())

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Time Series Analysis for Data Analysts

Trend:

Definition: The long-term movement or direction in the data (e.g., increasing sales over time).

Key Tools: Moving averages, trend lines.


Seasonality:

Definition: Regular patterns or cycles in the data that repeat at consistent intervals (e.g., higher sales during holidays).

Key Tools: Seasonal decomposition, Fourier transforms.


Stationarity:

Definition: A stationary time series has constant mean, variance, and autocorrelation over time.

Key Test: Augmented Dickey-Fuller (ADF) test.


Autocorrelation:

Definition: The correlation of a time series with its past values.

Key Tools: Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF).

Forecasting:

Common Models: ARIMA, SARIMA, Exponential Smoothing, Prophet.

Key Consideration: Split data into training and test sets for accurate forecasting.

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Top Excel Formulas Every Data Analyst Should Know

SUM():

Purpose: Adds up a range of numbers.

Example: =SUM(A1:A10)


AVERAGE():

Purpose: Calculates the average of a range of numbers.

Example: =AVERAGE(B1:B10)


COUNT():

Purpose: Counts the number of cells containing numbers.

Example: =COUNT(C1:C10)


IF():

Purpose: Returns one value if a condition is true, and another if false.

Example: =IF(A1 > 10, "Yes", "No")


VLOOKUP():

Purpose: Searches for a value in the first column and returns a value in the same row from another column.

Example: =VLOOKUP(D1, A1:B10, 2, FALSE)


HLOOKUP():

Purpose: Searches for a value in the first row and returns a value in the same column from another row.

Example: =HLOOKUP("Sales", A1:F5, 3, FALSE)


INDEX():

Purpose: Returns the value of a cell based on row and column numbers.

Example: =INDEX(A1:C10, 2, 3)


MATCH():

Purpose: Searches for a value and returns its position in a range.

Example: =MATCH("Product B", A1:A10, 0)


CONCATENATE() or CONCAT():

Purpose: Joins multiple text strings into one.

Example: =CONCATENATE(A1, " ", B1)


TEXT():

Purpose: Formats numbers or dates as text.

Example: =TEXT(A1, "dd/mm/yyyy")

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SQL Performance Tuning Tips

Indexing:

Tip: Create indexes on frequently queried columns to speed up search operations.

Consideration: Too many indexes can slow down write operations.


Avoid SELECT *:

Tip: Always specify only the columns you need in a query to reduce I/O overhead.


Use Joins Efficiently:

Tip: Use INNER JOIN instead of OUTER JOIN when possible to minimize unnecessary data retrieval.

Consideration: Be cautious with CROSS JOINs as they can produce large result sets.


Limit Results:

Tip: Use LIMIT or TOP to return only the necessary number of records for faster performance.


Optimize Subqueries:

Tip: Convert subqueries into JOINs where possible to improve readability and performance.


Use EXPLAIN:

Tip: Use the EXPLAIN plan to analyze query execution and identify bottlenecks.


Partitioning:

Tip: Partition large tables into smaller, more manageable pieces to improve query performance.


Avoid Functions on Indexed Columns:

Tip: Avoid applying functions (like LOWER, UPPER) on indexed columns, as it prevents the use of the index.

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Best Practices for Data-Driven Decision Making

Define Clear Objectives:

Tip: Start with well-defined business goals and questions to guide your analysis.

Consideration: Align analysis with strategic business objectives to ensure relevance.


Collect Accurate Data:

Tip: Ensure data is clean, accurate, and representative of the problem you're solving.

Consideration: Validate sources and avoid biased or incomplete datasets.


Visualize Data Effectively:

Tip: Use clear and simple visualizations to highlight key insights.

Consideration: Tailor visualizations to your audience for better comprehension.


Interpret Results with Context:

Tip: Always interpret data within the context of the business environment.

Consideration: Data should be viewed alongside domain knowledge and external factors.


Iterate and Refine:

Tip: Continuously refine your models and strategies based on feedback and new data.

Consideration: Data-driven decisions should evolve with changing market conditions.


Ensure Collaboration:

Tip: Foster collaboration between data analysts, stakeholders, and decision-makers.

Consideration: Encourage cross-functional communication to make informed decisions.


Measure Impact:

Tip: Measure the impact of your decisions and adjust strategies as needed.

Consideration: Track performance metrics to evaluate the success of your data-driven decisions.

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