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Stop Cleaning Data Manually 🛑
Most data scientists spend the majority of their time fighting with messy CSVs and inconsistent formats.
But the pros don’t do it manually. They build pipelines.
A data pipeline is your "set it and forget it" system for data preprocessing.
By using tools like Pandas for manipulation, Scikit-learn for chaining steps, and Dask for scaling, you can slash your manual workload by up to 70%.
Why you need this:
Speed: Go from raw data to insights in seconds.
Reliability: Eliminate human error in the cleaning process.
Reproducibility: Run the same logic on new data without rewriting code.
In a recent healthcare case study, automating this process helped a team predict patient readmission faster and more accurately than ever before.
Which tool is a permanent part of your toolkit?
1. Pandas 🐼
2. Scikit-learn ⚙️
3. Dask ☁️
Most data scientists spend the majority of their time fighting with messy CSVs and inconsistent formats.
But the pros don’t do it manually. They build pipelines.
A data pipeline is your "set it and forget it" system for data preprocessing.
By using tools like Pandas for manipulation, Scikit-learn for chaining steps, and Dask for scaling, you can slash your manual workload by up to 70%.
Why you need this:
Speed: Go from raw data to insights in seconds.
Reliability: Eliminate human error in the cleaning process.
Reproducibility: Run the same logic on new data without rewriting code.
In a recent healthcare case study, automating this process helped a team predict patient readmission faster and more accurately than ever before.
Which tool is a permanent part of your toolkit?
1. Pandas 🐼
2. Scikit-learn ⚙️
3. Dask ☁️
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Data visualization isn’t just about making charts—it’s about telling a story that drives decisions. Here are 15 essential tips to create impactful, clear, and engaging visualizations that your audience will actually understand and remember:
✅ Ask the right questions to uncover meaningful insights
✅ Choose the right chart to match your story
✅ Keep it simple—remove distracting fonts and elements
✅ Use consistent colors and make labels clear and visible
✅ Design for comprehension, not confusion
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Ex_Files_Distributed_Databases_with_Apache_Ignite.zip
100.7 KB
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A SQL query executes its statements in the following order:
1) FROM / JOIN
2) WHERE
3) GROUP BY
4) HAVING
5) SELECT
6) DISTINCT
7) ORDER BY
8) LIMIT / OFFSET
The techniques you implement at each step help speed up the following steps. This is why it’s important to know their execution order. To maximize efficiency, focus on optimizing the steps earlier in the query.
With that in mind, let’s take a look at some optimization tips:
1) Maximize the WHERE clause
This clause is executed early, so it’s a good opportunity to reduce the size of your data set before the rest of the query is processed.
2) Filter your rows before a JOIN
Although the FROM/JOIN occurs first, you can still limit the rows. To limit the number of rows you are joining, use a subquery in the FROM statement instead of a table.
3) Use WHERE over HAVING
The HAVING clause is executed after WHERE & GROUP BY. This means you’re better off moving any appropriate conditions to the WHERE clause when you can.
4) Don’t confuse LIMIT, OFFSET, and DISTINCT for optimization techniques
It’s easy to assume that these would boost performance by minimizing the data set, but this isn’t the case. Because they occur at the end of the query, they make little to no impact on its performance.
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Here are five of the most commonly used SQL queries in data science:
1. SELECT and FROM Clauses
- Basic data retrieval:
2. WHERE Clause
- Filtering data:
3. GROUP BY and Aggregate Functions
- Summarizing data:
4. JOIN Operations
- Combining data from multiple tables:
5. Subqueries and Nested Queries
- Advanced data retrieval:
1. SELECT and FROM Clauses
- Basic data retrieval:
SELECT column1, column2 FROM table_name;
2. WHERE Clause
- Filtering data:
SELECT * FROM table_name WHERE condition;
3. GROUP BY and Aggregate Functions
- Summarizing data:
SELECT column1, COUNT(*), AVG(column2) FROM table_name GROUP BY column1;
4. JOIN Operations
- Combining data from multiple tables:
SELECT a.column1, b.column2
FROM table1 a
JOIN table2 b ON a.common_column = b.common_column;
5. Subqueries and Nested Queries
- Advanced data retrieval:
SELECT column1
FROM table_name
WHERE column2 IN (SELECT column2 FROM another_table WHERE condition);
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