Data Science – Introduction to Data Science for Python

This Data Science tutorial aims to guide you to the world of data science and get you started with the basics, like what data science is, the history of Data Science, and Data Science Methodologies. Here, we will cover the Data Science Applications and the difference between Business Intelligence and Data Science. Along with this, we will discuss the life cycle of Data Science and Python Libraries.

So, let’s begin the Data Science Tutorial.

What is Data Science?

Before we start the Data Science Tutorial, we should find out what data science really is.

Data Science is a field that uses tools, coding, and thinking skills to find answers from data. In simple words, Data Science is the art and science of turning raw data into useful insights. It brings together three major areas:

• Math & Statistics: to understand data patterns.
• Computer Programming: to clean, organize, and process data.
• Business or Domain Knowledge: to ask the right questions and use results in real life.

Key steps used in Data Science:

• Data collection: Raw data is being gathered from various sources.
• Data Cleaning: It ensures that the data collected is accurate and ready for analysis.
• Data Analysis: Strategic and computational methods are applied to identify the trends, patterns, and connections.
• Data Visualization: Charts, graphs, and dashboards are created to present the data.
• Decision Making: Insights are used to create solutions, predict outcomes, and to implement strategies.

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History of Data Science

Through the recent hype that data science has picked up, we observe that it has been around for over thirty years. What one we could use as a synonym for practices like business analytics, business intelligence, or predictive modeling now refers to a broad sense of dealing with data to find a relationship within it. To quote a timeline, it would go something like this:

a. In the 90s

• 1960- Peter Naur uses the term as a substitute for computer science.
• 1974- Peter Naur publishes Concise Survey of Computer Methods, using the term in a survey of contemporary data processing methods.
• 1996- Biennial conference in Kobe; members of the IFCS (International Federation of Classification Societies include the term in the conference title.
• 1997- November- Professor C.F. Jeff Wu delivers inaugural lecture on the topic “Statistics=Data Science?”.

b. In the 2000s

• 2001- William S. Cleveland introduces data science as an independent discipline in the article Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics.
• 2002- April- The ICSU (International Council for Science): Committee on Data for Science and Technology (CODATA) starts Data Science Journal- this publication is to focus on issues of data systems- description, publication, application, and also legal issues.
• 2003- January- Columbia University publishes the journal The Journal of Data Science, a platform that allows data workers to exchange ideas.
• 2005- National Science Board publishes Long-lived Digital Data Collections: Enabling Research and Education in the 21st Century- this provides a new definition of the term “data scientists”.
• 2007- Jim Gray, Turing awardee, envisions data-driven science as the fourth paradigm of science.
• 2012- Harvard Business Review article attributes the coinage of the term to DJ Patil and Jeff Hammerbacher in 2008.
• 2013- IEEE launches a task force on Data Science and Advanced Analytics; the first European Conference on Data Analysis (ECDA) is organized in Luxembourg, and the European Association for Data Science (EuADS) comes into existence.
• 2014- IEEE launches first international conference, International Conference on Data Science and Advanced Analytics; General Assembly launches student-paid Bootcamp, The Data Incubator launches data science fellowship for free.
• 2015- Springer launches International Journal on Data Science and Analytics.

Methodologies of Data Science

In this Data Science Tutorial, we will cover the following Methodologies in data Science:

a. Machine Learning for Pattern Discovery

With this, clustering comes into play. This is an algorithm to use to discover patterns; an unsupervised model. When you don’t have parameters on which to make predictions, clustering will let you find hidden patterns within a dataset.

One such use case is to use clustering in a telephone company to determine tower locations for optimum signal strength.

b. Machine Learning for Making Predictions

When we have the data we need to train our machine, we can use supervised learning to deal with transactional data. Making use of machine learning algorithms, we can build a model and determine what trends the future will observe.

c. Predictive Causal Analytics

Causal analytics lets us make predictions based on a cause. This will tell us how probable an event is to occur in the future. One use case will be to perform such analytics on payment histories of customers in a bank. This tells us how likely customers are to reimburse loans.

d. Prescriptive Analytics

Predictive analysis will prescribe your actions and the outcomes associated with those. This intelligence lets it make decisions and modify those using dynamic parameters. For a use case, let us suggest the self-driving car by Google. With the algorithms in place, it can decide when to speed up or slow down, when to turn, and which road to take.

Have a look at – 30 Most Popular Data Science Interview Questions

Data Science Applications

Let’s see some applications in this Data Science Tutorial:

a. Image Recognition

Using the face recognition algorithm of data science, we can get a lot done. Did Facebook ever suggest that people tag in your pictures? Have you tried the search-by-image feature from Google? Do you remember scanning a barcode to log in to WhatsApp Web using your smartphone?

b. Speech Recognition

Siri, Alexa, Cortana, and Google Voice all make use of speech recognition to understand your commands. Attributing to issues like different accents and ambient noise, this isn’t always completely accurate, though intelligible most of the time. This facilitates luxury like speaking the content of a text to send, using your virtual assistant to set an alarm, or even use it to play music, inquire about the weather, or make a call.

c. Internet Search

Search engines like Google, DuckDuckGo, Yahoo, and Bing make good use of data science to make fast, real-time searching possible.

d. Digital Advertisements

Data science algorithms let us understand customer behaviour. Using this information, we can put up relevant advertisements curated for each user. This also applies to advertisements as banners on websites and digital billboards at airports.

e. Recommender Systems

Names like Amazon and YouTube will throw in suggestions about similar products aside or below as you browse through a product or a video. This enriches the UX(user experience) and helps retain customers and users. This will also take into account the user’s search history and wishlist.

Let’s explore the Future of Data Science – Data Science Career Prospects

f. Price Comparison Websites

Websites like Junglee and PriceDekho let us compare prices for the same products across different platforms. This facility lets you make sure you grab the best deal. These websites work in the domains of technology, apparel, and policy among many others, and use APIs and RSS feeds to fetch data.

g. Gaming

As a player levels up, a machine learning algorithm can improve or upgrade itself. It is also possible for the opponent to analyze the player’s moves and add an element of difficulty to the game. Companies like Sony and Nintendo make use of this.

h. Delivery Logistics

Freight giants like UPS, FedEx, and DHL use practices of data science to discover optimal routes, delivery times, and transport modes, among many others. A plus with logistics is the data obtained from the GPS devices installed.

i. Fraud and Risk Detection

Practices like customer profiling and past expenditures let us analyze whether there will be a failure. This lets banks avoid debts and losses.

Business Intelligence vs Data Science

Here, in this part of the Data Science Tutorial, we discuss Data Science Vs BI. Business intelligence and data science aren’t exactly the same thing.

• BI works on structured data; data science works on both- structured and unstructured data.
• Where BI focuses on the past and the present, data science considers the present and the future.
• The approach to BI is statistics and visualization; that to data science is statistics, machine learning, graph analysis, and NLP.
• Some tools for BI are Pentaho, Microsoft BI, and R; those for data science are RapidMiner, BigML, and R.

Let’s Explore the Difference Between Data Science vs Data Analytics

Data Science Life-Cycle

The journey with data science goes through six phases-

a. Discovery in Data Science

Before anything else, you should understand what the project requires. Also consider the specifications, the budget needed, and priorities. This is the phase where you frame the business problem and form initial hypotheses.

b. Data Preparation in Data Science

In the preparation phase, you will need to perform analytics in an analytical sandbox. This is for an entire project. You will also extract, transform, and load data into the sandbox.

c. Model Planning in Data Science

In the third phase, you choose the methods you want to work with to find out how the variables relate to each other. This includes carrying out Exploratory Data Analytics (EDA), making use of statistical formulae and visualization tools.

d. Model Building in Data Science

This phase includes developing datasets for training and testing. It also means you will have to analyze techniques like classification and clustering, and determine whether the current infrastructure will do.

e. Communicate results in Data Science

This is the second last phase in the cycle. You must determine whether your goals have been met. Document your findings, communicate with stakeholders, and label the project a success or failure.
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f. Operationalize in Data Science

In the last phase, you must craft final reports, technical documents, and briefings

This Data Science Tutorial is dedicated to Python. So, let’s start Data Science for Python.

Why Python for Data Science?

So, now you know what data science is all about. But why is Python the best choice for it? Here are a few reasons-

• Open-source and free.
• Easy to learn; intuitive.
• Fewer lines of code.
• Portability.
• Better productivity.
• Demand and popularity.
• Excellent online presence/ community.
• Support for many packages usable with analytics projects; can also use packages that can use code from other languages.
• It is faster than similar tools like R and MATLAB.
• Amazing memory management abilities.

Follow this link to know more about why we learn the Python Programming Language

Python 2.x or 3.x- Which should you go for?

Among a lot of other factors, the support for Python 2 ends officially on January 1^st, 2020, so the future belongs to Python 3. Also, 95% of the libraries for data science are done being migrated from Python 2 to Python 3. Apart from that, Python 3 is cleaner and faster.

Well, then what about Python 2? It has its own perks- it is rich with a large online community and plenty of third-party libraries, and some features are backwards-compatible and work with both versions.

With the perks of each version listed, make your choices.

Python Libraries for Data Science

For carrying out data analysis and other scientific computation, you will need any of the following libraries:

a. Python Pandas  

Pandas help us with munging and preparing data; it is great for operating on and maintaining structured data.

b. Python SciPy

SciPy (Scientific Python) stands on top of NumPy. With this library, we can carry out functionality like Linear Algebra, Fourier Transform, Optimization, and many others.

c. Python NumPy

NumPy (Numerical Python) is another library that lets us deal with features like linear algebra, Fourier transforms and advanced random number capabilities. One very important feature of NumPy is the n-dimensional array.

d. Python Matplotlib

Matplotlib will let you plot different kinds of graphs. These include pie charts, bar graphs, histograms, and even heat plots.

e. Python Scikit-learn

Scikit-learn is great for machine learning. It will let you statistically model and implement machine learning. The tools for these include clustering, regression, classification, and dimensionality reduction.

f. Python Seaborn

Seaborn is good with statistical data visualization. Making use of it, we can create useful and attractive graphics.

g. Python Scrapy

Scrapy will let you crawl the web. It begins on a home page and gets deeper within a website for information.

Follow this link to know more about Python Libraries in detail

Learning in Data Science

Before you begin with data science Tutorials, we suggest you brush up on the following:

• Variables in Python
• Operators in Python
• Dictionaries in Python
• Strings in Python
• Python Lists
• Python Tuples

So, this was all about the Data Science Tutorial. Hope you like our explanation.

Conclusion

Hence, we completed this Data Science Tutorial, in which we learned: what Data Science is, the history of Data Science, and Data Science Methodologies. In addition, we covered the Data Science Applications, BI Vs Data Science. At last, we discussed the Life-Cycle of Data Science and Python Libraries. This will get you started with Python.

Data Science with Python brings together the world of statistics, computer science, and domain knowledge. With Python, you can analyze data, build models, and even create visual stories from raw data.

It covers everything from understanding trends to predicting future outcomes. Python has become the backbone of modern data science workflows, used in finance, healthcare, e-commerce, and almost every industry today.

When working on data science with Python, you use libraries like Pandas for data manipulation, NumPy for numerical computing, and Scikit-learn for machine learning. These tools provide a high level of abstraction so that you can focus on solving the business problem instead of writing long code

Got something else to add in this Data Science Tutorial? Drop it in the comments below.

Related Topic- Data Science Interview Questions-Answers
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