Data Analytics With Python: Use Case Demo

Data is getting generated at a massive rate, by the minute. Organizations, on the other hand, are trying to explore every opportunity to make sense of this data. This is where Data analytics has become crucial in running a business successfully. It is commonly used in companies to drive profit and business growth. In this article, we’ll learn Data analytics using Python.

What is Data Analytics?

Data analytics is the process of exploring and analyzing large datasets to make predictions and boost data-driven decision making. Data analytics allows us to collect, clean, and transform data to derive meaningful insights. It helps to answer questions, test hypotheses, or disprove theories. 

Let’s understand the various applications of data analytics.

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Applications of Data Analytics

Data analytics is used in most sectors of businesses. Here are some primary areas where data analytics does its magic:

1. Data analytics is used in the banking and e-commerce industries to detect fraudulent transactions.
2. The healthcare sector uses data analytics to improve patient health by detecting diseases before they happen. It is commonly used for cancer detection.
3. Data analytics finds its usage in inventory management to keep track of different items.
4. Logistics companies use data analytics to ensure faster delivery of products by optimizing vehicle routes.
5. Marketing professionals use analytics to reach out to the right customers and perform targeted marketing to increase ROI.
6. Data analytics can be used for city planning, to build smart cities.

Types of Data Analytics

Data analytics can be broadly classified into 3 types:

1. Descriptive Analytics

It tells you what has happened. It can be done using an exploratory data analysis.

Example: Studying the total units of chairs sold and the profit that was made in the past.

2. Predictive Analytics

It tells you what will happen. It can be achieved by building predictive models.

Example: Predicting the total units of chairs that would sell and the profit we can expect in the future.

3. Prescriptive Analytics

It tells you how to make something happen. It can be done by deriving key insights and hidden patterns from the data.

Example: Finding ways to improve sales and profit of chairs.

The graph below represents the difficulty level and values the can be derived from the different types of data analytics.

Data Analytics Process Steps

There are primarily five steps involved in the data analytics process, which include:

1. Data Collection: The first step in data analytics is to collect or gather relevant data from multiple sources. Data can come from different databases, web servers, log files, social media, excel and CSV files, etc.
2. Data Preparation: The next step in the process is to prepare the data. It involves cleaning the data to remove unwanted and redundant values, converting it into the right format, and making it ready for analysis. It also requires data wrangling.
3. Data Exploration: After the data is ready, data exploration is done using various data visualization techniques to find unseen trends from the data.
4. Data Modeling: The next step is to build your predictive models using machine learning algorithms to make future predictions.
5. Result interpretation: The final step in any data analytics process is to derive meaningful results and check if the output is in line with your expected results.

Why Data Analytics Using Python?

There are many programming languages available, but Python is popularly used by statisticians, engineers, and scientists to perform data analytics.

Here are some of the reasons why Data Analytics using Python has become popular:

1. Python is easy to learn and understand and has a simple syntax.
2. The programming language is scalable and flexible.
3. It has a vast collection of libraries for numerical computation and data manipulation.
4. Python provides libraries for graphics and data visualization to build plots.
5. It has broad community support to help solve many kinds of queries.

Python Libraries for Data Analytics

One of the main reasons why Data Analytics using Python has become the most preferred and popular mode of data analysis is that it provides a range of libraries.

NumPy: NumPy supports n-dimensional arrays and provides numerical computing tools. It is useful for Linear algebra and Fourier transform.

Pandas: Pandas provides functions to handle missing data, perform mathematical operations, and manipulate the data.

Matplotlib: Matplotlib library is commonly used for plotting data points and creating interactive visualizations of the data.

SciPy: SciPy library is used for scientific computing. It contains modules for optimization, linear algebra, integration, interpolation, special functions, signal and image processing.

Scikit-Learn: Scikit-Learn library has features that allow you to build regression, classification, and clustering models.

Now, let’s look at how to perform data analytics using Python and its libraries.

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Data Analytics Using the Python Library, NumPy

Let’s see how you can perform numerical analysis and data manipulation using the NumPy library.

1. Create a NumPy array.

2. Access and manipulate elements in the array.

3. Create a 2-dimensional array and check the shape of the array.

4. Access elements from the 2D array using index positions.

5. Create an array of type string.

6. Using the arange() and linspace() function to evenly space values in a specified interval.

7. Create an array of random values between 0 and 1 in a given shape.

8. Create an array of constant values in a given shape.

9. Repeat each element of an array by a specified number of times using repeat() and tile() functions.

10. Create an identity matrix using eye() and identity() function.

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11. Create a 5x5 2D array for random numbers between 0 and 1.

12. Sum an array along the column.

13. Sum an array along the row.

14. Calculate the mean, median, standard deviation, and variance.

15. Sort an array along the row using the sort() function.

16. Append elements to an array using the append() function.

17. Delete multiple elements in an array.

18. Concatenate elements from 2 arrays.

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Data Analytics Using Python Libraries, Pandas and Matplotlib

We’ll use a car.csv dataset and perform exploratory data analysis using Pandas and Matplotlib library functions to manipulate and visualize the data and find insights.

1. Import the libraries.

2. Load the dataset using pandas read_csv() function.

3. Display the head of the dataset using the head() function.

4. Display the bottom 5 rows from the dataset using the tail() function.

5. Print summary statistics of the dataset using the describe() function.

6.Plot a histogram for all the variables.

7. Box plot to visualize the relationship between vehicle size and engine hp.

8. Build a pair plot using the seaborn library.

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9. Drop irrelevant columns from the dataset using drop() function.

 10. Use rename() function to rename the columns.

11. Print the total number of duplicate rows.

12. Remove the duplicate rows using the drop_duplicates() function.

13. Drop the missing values from the dataset.

14. Plot a histogram to find the number of cars per brand.

15. Draw a correlation plot between the variables.

Conclusion

Data is getting generated rapidly in various formats. And companies are relying on data analytics to derive valuable information and hidden insights from this data. After reading this ‘Data analytics using Python’ article, you would have learned what data analytics is and the various applications of data analytics. You also looked at the different types of data analytics and process steps. Finally, you performed data analytics using Python’s NumPy, Pandas, and Matplotlib libraries.

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