Python for Data Science

Data Science is one of the trending careers in the world. Many people are looking for opportunities in this domain and many innovations are taking place every day. Since Python is simple and user-friendly, it is opted by most beginners and data scientists for implementation purposes.

This article will guide you to learn the basics of data science and its implementation using Python. Let us begin with the introduction to the main topic “Data Science”.

What is Data Science?

Let us know more about data science, let us learn what data science is. Data Science is a field of study which involves deriving patterns and insights from the given raw data. It involves organizing, processing, visualizing, and analyzing big data, which can either be structured or unstructured. We use different mathematical and statistical operations, algorithms like machine learning, and other scientific methods.

Applications of Data Science

Data Science not only involves multiple disciplines for different operations, it is also used in multiple domains to solve problems. Let us some of the important ones:

1. Image Recognition/ Computer Vision

Image recognition is the ability of the software to recognize people, objects, animals, etc. This is used in many applications which include face recognition, scanning barcodes for logging in, using google lens to search by image, monitoring using drones, autonomous vehicles, etc. Large sets of image data from multiple objects are modeled, and algorithms are created. This is applied to newer images to get results.

2. Speech Recognition

Did you ever use a bot or device that listens to understands your voice message and gives corresponding output? Are you remembering Google assistance, Siri, Alexa, Cortana? Yes! All these employ speech recognition and do give an accurate response immediately.

3. Search Engines

You would have seen that you get a lot of recommendations when you are typing something on any of the search engines like Google, Duckduckgo, Yahoo, and Bing. Did you ever observe that most of the time the recommendations relate to our previous search? This is where data science comes into play making the search fast and user-friendly.

4. Advertisements

Data science algorithms are also used in making the process of curing the advertisements for each user smarter. It understands customer behavior and gives related advertisements. This also applies to the advertisements we see on websites and billboards at airports.

5. Recommender Systems

Did you ever get notifications from apps like Amazon about a product that is similar to the previous searches? This is a method employed to enrich the user experience and to retain them.

6. Health Care Services

In the healthcare industry, there is a variety of data. All this data needs to be analyzed to produce insights that can save cost and time. Different algorithms are also used to determine the presence of diseases.

7. Price Comparison

There are websites like Junglee and PriceDekho that facilitate the comparison of prices for the same products sold on different platforms. This also includes data science that helps us grab the best deal.

8. Intelligent Games

There are games that employ some machine learning algorithms to increase the difficulty as the level goes up. It also facilitates the user to analyze the opponent’s moves.

9. Delivery Logistics

Transport companies use data science to find optimal routes, delivery times, and transport modes. The data for these operations is obtained from GPS devices.

10. Fraud and Risk Detection

This is used by banks to avoid the debts of customers and losses. Customer profiles and past expenditures, other financial commitments, and many socio-economic indicators. are organized and used to analyze the probability of failure. Data science is used here to minimize the loss for the financial organization.

11. Efficient Energy Management

We all tend to minimize the usage of energy for different purposes like cost, availability of energy sources, etc. Even companies tend to manage the various phases of energy production efficiently. For this, the production methods, the storage and distribution mechanisms, the customer’s consumption, etc are optimized. And data science is used for easier analysis.

Life Cycle of Data Science

Every project implemented in Data Science involves the following six phases:

1. Understanding the project

The first step is to understand the project requirements. Only when we do this, we can move forward to implement it. This includes finding specifications, budgets, and priorities. In this phase, we form the problem and the initial hypotheses.

2. Preparation of Data

Data is an important part of any problem. This step involves performing analysis by loading, extracting, and transforming.

3. Planning the Model

In the phase, the methods to be performed are chosen and a correlation between different variables is found out. This includes applying some statistical and visualization operations like computing the Exploratory Data Analytics (EDA).

4. Building the Model

In this phase, we develop the datasets for training and testing. Depending on the data and the requirement, we also need to identify the techniques like classification and clustering.

5. Analyzing the results

This phase involves finding if the requirements are met. This also includes gathering and documenting the results, sharing the results with the stakeholders, and labels the project as a success or not.

6. Operationalizing

This is the last phase. In this, you craft final reports, documents, and briefings of the operations, results, and other important information.

Why Python for Data Science? Which version to opt for?

For data science, Python is a preferred language by most data scientists and beginners. These are some of the advantages of the Python programming language:

1. Python is open-source and cross-platform.

2. It is simple, readable, and involves only a few lines of code. It is also easy to learn and it helps beginners and experts to concentrate on the concepts rather than the code.

3. Its execution is also faster compared to the other programming languages like MATLAB and R.

4. It has a wide range of libraries and packages which have tools for data science purposes. These provide different methods that can be used for data manipulation, visualizations, applying algorithms, etc. We will discuss these in the next section.

You would also be confused about whether to use the Python 2 or Python3 version. Python 3 is the latest one so it is the future and it has 95% of data science Python 2 libraries. In addition, Python 3 is cleaner and faster.

Python2 also has its pros like it has a large active community and a lot of third-party libraries. Some features work with both versions. It is your priority to choose a version.

Python Modules used for Data Science

We will see some of the important Python libraries for data science.

1. Pandas

It is a library used for the analysis, manipulation, and visualization of large sets of data. This can be structured (data frames) or time-series data and can also import data from spreadsheets. It allows us to:

a. Rename, index, manipulate, sort, merge data frames

b. Update, Add, Delete data

c. Handling missing data, duplicates, and redundant data

d. Get statistical information and other insights

2. NumPy

NumPy package is a package with different mathematical operations. It provides tools to build arrays, matrices, and different methods to perform on these. We can perform:

a. Adding, multiplying, slicing, reshaping arrays, and many more

b. Advanced operations like stacking arrays, splitting, and broadcasting arrays

c. Algebraic operations, statistical operations, etc.

d. Operations on DateTime values

3. Matplotlib

Matplotlib is a library used for data visualization. It is used for the plotting of graphs and sharing them in a variety of hard copy formats. It allows us to draw line plots, charts, graphs, pie charts, bar plots, scatterplots, histograms, etc. And also add some properties to the plots like title, labels, color, etc. It can be used in different GUI toolkits like python scripts, web applications, shell, etc.

4. SciPy

Scipy is an extension to the NumPy module. It is used for scientific and technical operations like linear algebra, optimization, integration, statistics, etc. Also, it can perform scientific programming operations like calculus, ordinary differential equations, signal processing, etc.

5. Scikit-learn

It is a library that contains different methods to perform supervised and unsupervised ML algorithms like SVMs, random forests, clustering, etc. It also has tools for data analysis and data mining.

6. Seaborn

Seaborn is another library used for visualization purposes. It is an extension to Matplotlib. It has high-level interfaces, advanced features, and styles. We can determine relationships between multiple variables, analyze univariate or bivariate distributions plots, etc.

7. Scrapy

Scrapy is a library used for crawling through the web. It can extract data from the websites, beginning from the home page, and goes deeper within a website for information.

Conclusion

In this article, we got introduced to data science and its implementation using Python. Hope this article helped you gain your knowledge on data science. Happy learning!