1. Introduction of Data Science

How is Machine Learning the Driving Force behind the Future of Data Science?

Data Science is expanding due to the immense contributions made by machine learning. It has improved the data science scenario in the following ways –

1. Advanced Personalisations

Billions of users around the world are using smartphones, watches as well as other electronic devices. Customers generate such a colossal amount of data creating a huge potential for the industry to have a better understanding.

Therefore, companies are able to maximize value for themselves as well as improve the understanding of their user-base thoroughly.

2. Giving Advanced Search Engine Results to the User

Machine Learning algorithms are capable of making search results much more appealing to the user. Using Google’s advanced machine learning algorithms, we can get new content based on previous search history.

These results are predicted to grow much better in the future owing to immense researches that are ongoing in the field of machine learning.

3. Code Free Environments

With the help of Machine Learning Tools, softwares are evolving at a rate such that a Ph.D. is no longer required for understanding the depth of these operations.

This is a result of a constant evolution wherein functions like pytorch and TensorFlow can be utilized to perform rapid prototyping of data science solutions.

4. Quantum Computing

The potential for quantum computing and data science is huge in the future. Machine Learning can also process the information much faster with its accelerated learning and advanced capabilities.

Based on this, the time required for solving complex problems is significantly reduced. This will boost the health-care industry massively.

1. Introduction of Data Science

Future of Data Science

Data Science is a colossal pool of multiple data operations. These data operations also involve machine learning and statistics. Machine Learning algorithms are very much dependent on data. This data is fed to our model in the form of training set and test set which is eventually used for fine-tuning our model with various algorithmic parameters.

By all means, advancement in Machine Learning is the key contributor towards the future of data science.

In particular, Data Science also covers:

  • Data Integration.
  • Distributed Architecture.
  • Automating Machine learning.
  • Data Visualisation.
  • Dashboards and BI.
  • Data Engineering.
  • Deployment in production mode
  • Automated, data-driven decisions.

i. Data Science currently does not have a fixed definition due to its vast number of data operations. These data operations will only increase in the future. However, the definition of data science will become more specific and constrained as it will only incorporate essential areas that define the core data science.

ii. In the near future, Data Scientists will have the ability to take on areas that are business-critical as well as several complex challenges. This will facilitate the businesses to make exponential leaps in the future. Companies in the present are facing a huge shortage of data scientists. However, this is set to change in the future.

In India alone, there will be an acute shortage of data science professionals until 2020. The main reason for this shortage is India is because of the varied set of skills required for data science operations.

There are very few existing curricula that address the requirements of data scientists and train them. However, this is gradually changing with the introduction of Data Science degrees and bootcamps that can transform a professional from a quantitative background or a software background into a fully-fledged data scientist.

Data Science Future Career Predictions

According to IBM, there is a predicted increase in the data science job openings by 364,000 to 2,720,000. You can learn more about the demand prediction by IBM – Data Scientists Demand Prediction for 2020

We can summarize the trends leading to the future of data science in the following three points –

  • The increase of complex data science algorithms will be subsumed in packages in a magnitude making them quite easier to deploy. For example, a simple machine learning algorithms like decision trees which required huge resources in the past can now be easily deployed.
  • Large Scale Enterprises are rapidly adopting machine learning for driving their business in several ways. Automation of several tasks is one of the key future goals of the industries. As a result, they are able to prevent losses from taking place.
  • As discussed above, the prevalence of academic programs and data literacy initiatives are allowing students to get exposed to data related disciplines. This is imparting a competitive edge to the students in order to help them stay ahead of the curve.
1. Introduction of Data Science

12 Top Data Science Skills

A domain of Important Skills for Data Scientists

We can divide the required set of Data Science skills into 3 domains

  • Analytics
  • Programming
  • Domain Knowledge

Domain of Important Skills for Data Scientists

This is on a very abstract level in the taxonomy. Below, we are discussing some Data Science Skills in demand

  1. Statistics
  2. Programming skills
  3. Critical thinking
  4. Knowledge of AI, ML, and Deep Learning
  5. Comfort with math
  6. Good Knowledge of Python, R, SAS, and Scala
  7. Communication
  8. Data Wrangling
  9. Data Visualization
  10. Ability to understand analytical functions
  11. Experience with SQL
  12. Ability to work with unstructured data

a. Statistics

As a data scientist, you should be capable of working with tools like statistical tests, distributions, and maximum likelihood estimators.

A good data scientist will realize what technique is a valid approach to her/his problem. With statistics, you can help stakeholders take decisions and design and evaluate experiments.

b. Programming Skills

Good skills in tools like Python or R and a database querying language like SQL will be expected of you as a data scientist. You should be comfortable carrying out different tasks of programming activities. You will be expected to deal with both computational and statistical aspects of it.

c. Critical Thinking

Can you apply an objective analysis of facts to a problem or do you render opinions without it? A data scientist should be able to abstract the paydirt of the problem and ignore irrelevant details.

d. Knowledge of Machine Learning, Deep Learning, and AI

Machine Learning is a subset of Artificial Intelligence that uses statistical methods to make computers capable of learning with data. For this, they shouldn’t need to be explicitly programmed.

With Machine Learning, things like self-driving cars, practical speech recognition, effective web search, and understanding of the human genome are made possible.

Deep Learning is a part of a family of machine learning methods. It is based on learning data representations; learning can be unsupervised, semi-supervised, or supervised.

e. Comfort With Math

A data scientist should be able to develop complex financial or operational models that are statistically relevant and can help shape key business strategies.

f. Good knowledge of Python, R, SAS, and Scala

Working as a data scientist, a good knowledge of the languages Python, SAS, R, and Scala will help you a long way.

g. Communication

Skilful communication- both verbal and written, is key. As a data scientist, you should be able to use data to communicate effectively with stakeholders. A data scientist stands at the intersection of business, technology, and data.

Qualities like eloquence and storytelling abilities help the scientist dilute complex technical information into something simple and accurate to the audience. Another task with data science is to communicate to business leaders how an algorithm arrives at a prediction.

h. Data Wrangling

We have seen this with Python Data Wrangling. A lot of data you will be working on will be messy. Values could be missing, there could be inconsistent formatting with dates and strings. You will need to clean and wrangle your data.

i. Data Visualization

This is an essential part of data science, of course, as it lets the scientist describe and communicate their findings to technical and non-technical audiences. Tools like Matplotlib, ggplot, or d3.js let us do just that. Another good tool for this is Tableau.

j. Ability to Understand Analytical Functions

Such functions are locally represented by a convergent power series. An analytic function has its Taylor series about x0 for every x0 in its domain converge to the function in a neighbourhood.

These are of types real and complex- both infinitely differentiable. A good understanding of these helps with data science.

k. Experience with SQL

SQL is a fourth-generation language; a domain-specific language designed to manage data stored in an RDMS (Relational Database Management System) and for steam processing in an RDSMS (Relational Data Stream Management System).

We can use it to handle structured data in situations where variables of data relate to each other.

l. Ability To Work With Unstructured Data

If you are comfortable with unstructured data from sources like video and social media and can wrangle it, it is a plus for your journey with data science.

So, this was all in General and Demanding Data Science Skills. Hope you like our explanation.

1. Introduction of Data Science

What is the Purpose of Data Science?

In this lesson, we will go through the role that a Data Scientist plays. There is a veil of mystery surrounding Data Science. While the buzzword of Data Science has been circulating for a while, very few people know about the real purpose of being a Data Scientist.

We will go through the various responsibilities that a Data Scientist must fulfill and understand as to what industries seek from employing Data Scientists. After this, we will look at various types of industries which employ Data Scientists to make better decisions. So, let’s explore the purpose of Data Science.

Purpose of Data Science

The principal purpose of Data Science is to find patterns within data. It uses various statistical techniques to analyze and draw insights from the data. From data extraction, wrangling and pre-processing, a Data Scientist must scrutinize the data thoroughly.

Then, he has the responsibility of making predictions from the data. The goal of a Data Scientist is to derive conclusions from the data. Through these conclusions, he is able to assist companies in making smarter business decisions.

We will divide this blog in various sections to understand the role of a Data Scientist in more detail.

Why Data Matters

Data is the new electricity. We are living in the age of the fourth industrial revolution. This is the era of Artificial Intelligence and Big Data. There is a massive data explosion that has resulted in the culmination of new technologies and smarter products.

Around 2.5 exabytes of Data is created each day. The need for data has risen tremendously in the last decade. Many companies have centered their business on data. Data has created new sectors in the IT industry. However,

  1. Why do we need Data?
  2. Why do industries need Data?
  3. What makes data a precious commodity?

The answer to these questions lies in the way companies have sought to transform their products.

Data Science is a very recent terminology. Before Data Science, we had statisticians. These statisticians experienced in qualitative analysis of data and companies employed them to analyze their overall performance and sales.

With the advent of a computing process, cloud storage, and analytical tools, the field of computer science merged with statistics. This gave birth to Data Science.

Early data analytics based on surveying and finding solutions to public problems. For example, a survey regarding a number of children in a district would lead to a decision of development of the school in that area.

With the help of computers, the decision-making process has been simplified. As a result, computers could solve more complex statistical problems. As Data started to proliferate, companies started to realize its value.

Its importance reflected in the many products designed to boost customer experiences. Industries sought experts who could tap the potential that data holstered. Data could help them make the right business decisions and maximize their profits.

Moreover, it gave the company an opportunity to examine and act according to customer behavior based on their purchasing patterns. Data helped companies boost their revenue model and helped them craft a better quality product for clients.

Data is to products what electricity is to household gadgets. We need data to engineer the products that cater to the users. It is what drives the product and makes it usable. A Data Scientist is like a sculptor.

He chisels the data to create something meaningful out of it. While it can be a tedious task, a Data Scientist needs to have the right expertise to deliver the results.

Why is Data Science Important?

Data creates magic. Industries need data to help them make careful decisions. Data Science churns raw data into meaningful insights. Therefore, industries need data science. A Data Scientist is a wizard who knows how to create magic using data.

A skilled Data Scientist will know how to dig out meaningful information with whatever data he comes across. He helps the company in the right direction. The company requires strong data-driven decisions at which he’s an expert.

The Data Scientist is an expert in various underlying fields of Statistics and Computer Science. He uses his analytical aptitude to solve business problems.

Data Scientist is well versed with problem-solving and is assigned to find patterns in data. His goal is to recognize redundant samples and draw insights from it. Data Science requires a variety of tools to extract information from the data.

A Data Scientist is responsible for collecting, storing and maintaining the structured and unstructured form of data.

While the role of Data Science focuses on the analysis and management of data, it is dependent on the area that the company is specialized in. This requires the Data Scientist to have domain knowledge of that particular industry.

Purpose of Data Centric Industries

As mentioned above, companies need data. They need it for their data-driven decision models and creating better customer experiences. In this section, we will explore the specific areas that these companies focus on in order to make smarter data-driven decisions.

i. Data Science for Better Marketing

Companies are using Data to analyze their marketing strategies and create better advertisements. Many times, businesses spend an astronomical amount on marketing their products. This may at times not yield expected results.

Therefore, by studying and analyzing customer feedback, companies are able to create better advertisements. The companies do so by carefully analyzing customer behavior online. Also, monitoring customer trends helps the company to get better market insights.

Therefore, businesses need Data Scientists to assist them in making strong decisions with regards to marketing campaigns and advertisements.

ii. Data Science for Customer Acquisition

Data Scientists help the company to acquire customers by analyzing their needs. This allows the companies to tailor products best suited for the requirements of their potential customers. Data holds the key for companies to understand their clients.

Therefore, the purpose of a Data Scientist here is to enable companies to recognize clients and help them deliver the needs of their customers.

iii. Data Science for Innovation

Companies create better innovations with an abundance of data. The Data Scientists aid in product innovation by analyzing and creating insights within the conventional designs.

They analyze customer reviews and help the companies craft product that sits perfectly with the reviews and feedback. Using the data from customer feedback, companies make decisions and take proper action in the right direction.

iv. Data Science for Enriching Lives

Customer data is key to making their lives better. Healthcare industries use the data available to them to assist their customers in their everyday life.

Data Scientists in these type of industries have the purpose of analyzing the personal data, health history and create products that tackle the problems faced by customers.

From the above instances of data-centric companies, it is clear that each company uses data differently. The use of data varies as per company requirements. Therefore, the purpose of Data Scientists depends on the interests of the company.

Other Skills for Data Scientist

Now, in this blog on the purpose of data science, we will see what other skills a Data Scientist will require. In this section, we will explore how a Data Scientists job stretches beyond analyzing and drawing insights from the data.

More than using statistical techniques to draw conclusions, a Data Scientists goal is to communicate his results with the company. A Data Scientist need not only be proficient in number crunching but should also capable of translating the mathematical jargons for taking proper business decisions.

For example – Consider a Data Scientist analyzing monthly sales of the company. He uses various statistical tools to analyze and draw conclusions from the data. In the end, he obtains results that he needs to share with the company.

The Data Scientist needs to know how to communicate results in a very concise and simple manner. The technical results and processes may not be understood by the people managing sales and distribution.

Therefore, a Data Scientist must be able to story tell. The storytelling of data will allow him to transfer his knowledge across to the management team without any hassle. Therefore, it broadens the purpose of a Data Scientist.

Data Science is an agglomeration of management and IT. The purpose of Data Scientist not only limited to statistical processing of data but also managing and communicating data to help companies make better decisions.

So, this was all in the purpose of Data Science. Hope you liked our lesson.

1. Introduction of Data Science

Work of Data Scientist ?

Data Scientists work in a variety of fields. Each is crucial to finding solutions to problems and requires specific knowledge. These fields include data acquisition, preparation, mining and modeling, and model maintenance. Data scientists take raw data, turn it into a goldmine of information with the help of machine learning algorithms that answer questions for businesses seeking solutions to their queries. Each of the field is explained in this introduction to data science tutorial, starting with,

  • Data Acquisition: Here, data scientists take data from all its raw sources, such as databases and flat-files. Then, they integrate and transform it into a homogenous format, collecting it into what is known as a “data warehouse,” a system by which the data can be used to extract information from easily. Also known as ETL, this step can be done with some tools, such as Talend Studio, DataStage and Informatica.
  • Data Preparation: This is the most important stage, wherein 60 percent of a data scientist’s time is spent because often data is “dirty” or unfit for use and must be scalable, productive and meaningful. In fact, five sub-steps exist here:
  1. Data Cleaning: Important because bad data can lead to bad models, this step handles missing values and null or void values that might cause the models to fail. Ultimately, it improves business decisions and productivity.
  2. Data Transformation: Takes raw data and turns it into desired outputs by normalizing it. This step can use, for example, min-max normalization or z-score normalization.
  3. Handling Outliers: This happens when some data falls outside the scope of the realm of the rest of the data. Using exploratory analysis, a data scientist quickly uses plots and graphs to determine what to do with the outliers and see why they’re there. Often, outliers are used for fraud detection.
  4. Data Integration: Here, the data scientist ensures the data is accurate and reliable.
  5. Data Reduction: This compiles multiple sources of data into one, increases storage capabilities, reduces costs and eliminates duplicate, redundant data.
  • Data Mining: Here, data scientists uncover the data patterns and relationships to take better business decisions. It’s a discovery process to get hidden and useful knowledge, commonly known as exploratory data analysis. Data mining is useful for predicting future trends, recognizing customer patterns, helping to make decisions, quickly detecting fraud and choosing the correct algorithms. Tableau works nicely for data mining.
  • Model Building: This goes further than simple data mining and requires building a machine learning model. The model is built by selecting a machine learning algorithm that suits the data, problem statement and available resources.
    Machine learning algorithms used by Data scientists
    There are two types of machine learning algorithms: Supervised and Unsupervised:
  1. Supervised: Supervised learning algorithms are used when the data is labeled. There are two types:
  • Regression: When you need to predict continuous values and variables are linearly dependent, algorithms used are linear and multiple regression, decision trees and random forest
  • Classification: When you need to predict categorical values, some of the classification algorithms used are KNN, logistic regression, SVM and Naïve-Bayes
  • Unsupervised: Unsupervised learning algorithms are used when the data is unlabeled, there is no labeled data to learn from. There are two types:
  • Clustering: This is the method of dividing the objects which are similar between them and dissimilar to others. K-Means and PCA clustering algorithms are commonly used.
  • Association-rule analysis: This is used to discover interesting relations between variables, Apriori and Hidden Markov Model algorithm can be used
  • Model Maintenance: After gathering data and performing the mining and model building, data scientists must maintain the model accuracy. Thus, they take the following steps:
  1. Assess: Running a sample through the data occasionally to make sure it remains accurate
  2. Retrain: When the results of the reassessment aren’t right, the data scientist must retrain the algorithm to provide the correct results again
  3. Rebuild: If retraining fails, rebuilding must occur.

As you can see, data science is a complex process of various steps taking massive effort to achieve continuous, excellent results.

let’s look at a few examples of data science at work in the next section of the data science tutorial.

Two Examples of Data Science :

Data science uses its raw data to help solve problems. In each of these two cases, data helped solve a question plaguing people – in the first, a bank needed to understand why customers were leaving, this example focuses on data mining using Tableau. In the second, curiosity existed about what countries had the highest happiness rates, this example focuses on model building. Without data science, the answers couldn’t be found.

1st Example: Customer Exit Rate at a Bank

Data Preparation – Data Cleaning - Bank example

Here, a bank is doing a bit of data cleaning using Python. The customer loads a CSV file and discovers missing values in some subsets, such as the geography field. In this case, the data scientist needs to fill in the empty values with something to even out the data set, so the data is filled in with the “mean” score by writing a piece of code to do so. Otherwise, statistical data won’t work.

Data preparation - data cleaning - empty string in geography column

A data scientist can take other steps when data is missing, however. For example, one could drop the entire row – but that’s quite drastic and may skew the results of the study.

Data preparation - Data cleaning - data sets

If all the columns are empty, though, one can drop those. In addition, when 10 to 20 rows exist, and five to seven are blank, one can drop the five to seven without worrying that the results will change much.

After the data is cleaned, the data scientist is ready to use the data for data mining.

Data Mining using Tableau

Now, the data scientist uses Tableau to look at the exit rate of the bank’s customers based on gender, credit card holding and geography to see if these are affecting that rate.

Tableau uses a drag-and-drop system to analyze data, so, to analyze gender first, the data scientist puts “Exited” into the “Dimensions” section of Tableau and “Gender” into its “Measures” section.

Data mining using tableau - identify fields

This creates two columns, one for males and one for females, and two values, 0 for those who didn’t exit, and one for those who did.

Excel sheet - customers who exit

Then, a bar graph shows the percentages of the values. The data reveals a difference between females and males.

Bar chart for customers who exited

Doing the same for credit cards shows no impact, but geography also shows impact.

Data mining using tableau - basis of geography

As a result, the study shows that the bank should consider the gender and location of its customers when analyzing how it can better retain them. Thanks to data science, then, the bank learns important information about client behavior. In the next section of the introduction to data science tutorial let’s look at some of the practical data science applications and examples.

2nd example: Predicting World Happiness

Here’s the next example of data science use and application that you’ll learn in the introduction to data science tutorial. Predicting world happiness sounds like an impossible goal, no? Thanks to data science, it’s not! Rather, using multiple linear regression model building, it’s possible to assess it. In this introduction to data science tutorial, we will see how.

To do this, one first must ascribe values. In this case, they are happiness rank, happiness value, country, region, economy, family, health, freedom, trust, generosity and dystopian residual. Not all need to be used, but some must be to make and train the model.

Using Python, the data scientist imports libraries such as pandas, numpys, and sklearns. Data is imported as CSV files from the years 2015, 2016 and 2017. Next, the scientist can concatenate the three data or build one model for each CSV. Ultimately, the head() shows the top countries with the highest happiness score.

Plots and graphs arise in Python to show which countries are the happiest and which are less happy. A scatterplot shows the correlation between happiness rank and happiness score; it’s inversely correlated. More plots show that they convey the same message, so the happiness rank score can be dropped.