Unit III DEVELOPING ANALYTICAL TALENT
BUSINESS ANALYTICS
By
Mr. Basavaraj M. Naik M.Com, UGC NET, KSET
Teaching Assistant
Department of Studies in Commerce
Rani Channamma University Belagavi
Post- Graduate Centre, Jamkhandi

UNIT III Covers….
Meaning of Data Science, Features, Significance; Data Science
Resources - Professional Resources and Career Building Resources.
Data Science Application in Real World Scenario.
Big data- Meaning, Issues, Techniques.
Becoming Data Scientist- Features, Roles, Types, and Career Paths.

Scientific Thinking: is a type of knowledge seeking involving intentional information seeking, including asking
questions, testing hypothesis, making observations, recognizing patterns etc.
Statistical Methods:It all comes down to using the right methods for statistical analysis, which is how we
process and collect samples of data to uncover patterns and trends. For this analysis, there are five to choose
from: mean, standard deviation, regression, hypothesis testing, and sample size determination
Computer engineering (CoE or CpE) is a branch of engineering that integrates several fields of computer
science and electronic engineering required to develop computer hardware and software.
Innovative technologies A technological innovation is a new or improved product or process whose
technological characteristics are significantly different from before.

Data is a commodity which is wrapped with a process to make it valuable. Data science is a
process to extract value from data in all its forms. Under this process, data from all its forms
is compared and fine data is fetched for the further action. It basically refers to the collective
processes, scientific theories, technologies, analysis, knowledge base and tools.

Using Data Science approach, scientists apply machine learning algorithms
to numbers, text, audio, video, images and more to produce artificial
intelligence systems.
There is an overall process of data science where engineering is performed
over the raw data by manipulating and cleansing it to make it valuable and
then validated model is deployed ( utilised) by using this processed data.

Broadly, Data Science can be defined as the study of data, where it comes from,
what it represents, and the ways by which it can be transformed into valuable
inputs and resources to create business and IT strategies.
The following can be considered as examples of data science. Such as;
Identification and prediction of disease, optimizing shipping and logistics
routes in real-time, detection fraud, healthcare recommendations,
automating digital ads , etc. Data Science helps these sectors in various ways.

Characteristics of Data Science
The characteristics are as follows:

1. Business Understanding
It is the most important characteristic unless you understand the business; you
cannot make a good model even if you have good knowledge of machine learning
algorithms or statistical skills. A data scientist needs to understand the business
requirement and develop analytics according to them. So, domain knowledge of the
business also becomes important or helpful.

2. Intuition (Realisation)
Although the math involved is proven and foundational, a data scientist needs to
pick the right model with the right accuracy as all models will not give up the same
results. So a data scientist needs to feel when a model is ready for production
deployment (effective action) They also need the intuition to know at what point the
production model is outdated and needs to change respond to changing business
environment.

3. Curiosity
Data Science is not a new field. It has been there before also, but the progress
being made in this field is very fast. New methods to solve familiar problems
are being developed constantly, so, as a data scientist, curiosity to learn
emerging technologies becomes very important.

1. Business Intelligence for Making Smarter Decisions
Data science, it has transformed itself to become a more dynamic field. Data Science
has rendered Business Intelligence to incorporate a wide range of business
operations.
With the massive increase in the volume of data, businesses need data scientists to
analyze and derive meaningful insights (An example of insight is what you can have about someone's
life after reading a biography. An example of insight is understanding how a computer works.) from the data.
The meaningful insights will help the data science companies to analyze information
at a large scale and gain necessary decision-making strategies. The process of
decision making involves the evaluation and assessment of various factors involved
in it.

2. Making Better Products
Companies should be able to attract their customers towards products. They need to develop
products that suit the requirements of customers and provide them with guaranteed satisfaction.
Therefore, industries require data to develop their product in the best possible way.
For example – Airbnb ( An American company that manages the online market for housing,
mainly for holiday rental homestays and tourism activities. ) uses data science to improve its
services The data generated by the customers, is processed and analyzed. It is then used by
Airbnb to address the requirements and offer premier facilities to its customers.

3. Managing Businesses Efficiently
With Data Science, businesses can manage themselves more efficiently. Both large
scale businesses and small startups can benefit from data science in order to grow
further.
Using data science, businesses can also foster leadership development by tracking
the performance, success rate, and other important metrics. With workforce
analytics, industries can evaluate what is best working for the employees.
For example – Data Science can be used to monitor the performance of employees.
Using this, managers can analyze the contributions made by the employees and
determine when they should be promoted, managing their perks, etc.

4. Predictive Analytics to Predict Outcomes
Predictive analytics is the most important part of businesses. With the advent of advanced
predictive tools and technologies, companies have expanded their capability to deal with
diverse forms of data.
There are various applications of predictive analytics in businesses such as customer
segmentation, risk assessment, sales forecasting, and market analysis.

5. Leveraging ( hold or control)Data for Business Decisions
In the past, many businesses would take poor decisions due to the lack
of surveys or sole reliance on ‘gut feelings’. It would result in some
disastrous decisions leading to losses in millions.
However, with the presence of a plenty of data and necessary data tools,
it is now possible for the data industries to make calculated data-driven
decisions.

6. Assessing Business Decisions
After making decisions through the forecast of the future occurrences, it is a
requirement for the companies to assess them. This is possible through several
hypothesis testing tools.
After implementing the decisions, businesses should understand how these
decisions affect their performance and growth. If the decision leads to any negative
factor, then they should analyze it and eliminate the problem that is slowing down
their performance.

7. Automating Recruitment Processes
Some major businesses can even attract thousands of resumes for a
position. In order to make sense of all of these resumes and select the right
candidate, businesses make use of data science.
The data science technologies like image recognition are able to convert
the visual information from the resume into a digital format. It then
processes the data using various analytical algorithms like clustering and
classification to churn out the right candidate for the job.

Data Science Examples
Demand prediction for the manufacturing industry
The first data science real-life example is the manufacturing industry. Many manufacturers depend on data
science to create forecasts of product demand. It helps them in optimizing supply chains and delivering orders
without risk of over/under-ordering.
Data science can make a lot of savings for your manufacturing company especially in supply chain
optimization. Here are some benefits of data science implementation into your company:
● It minimizes the risk that parts won’t be delivered and stocked on time.
● Data science in supply chain optimization takes into consideration many factors that can have an
influence on the entire process, for example, shipping costs, weather, material availability, market
scarcity, and many more.
● Your company will be able to analyze the needs and behavior of customers using data analysis. The
results of this analysis are crucial in understanding what products enjoy the highest demand on the
market

Applications of Data Science
Here in the introduction to data science, we have cleared about data science applications that it
is huge. It’s required in every field. Here are examples of a few sectors where data science can
be used or being used actively.

1. Marketing
There is a huge scope ain marketing; for example, Improved Pricing strategy
Companies like Uber, e-commerce companies can use data science-driven
pricing, increasing their profits.
2. Healthcare
Using wearable data to prevent and monitor health problems. The data
generated from the body can be used in healthcare to prevent future
emergencies.

3. Banking and Finance
Data science uses in the banking sector for fraud detection, which can help
reduce the Non-Performing Assets of banks.
4. Government Policies
The Government can use data science to prepare better policies to cater to the
needs of the people and what they want using the data they can get by
conducting surveys and others from other official sources.

Advantages and Disadvantages of Data Science
Advantages
Some of the advantages are as follows:
● It helps us to get insights from historical data with its powerful tools.
● It helps to optimize the business, hire the right persons and generate more
revenue, as using data science helps you make better future decisions for the
business.
● Companies can develop and market their products better as they can better
select their target customers.
● Introduction to Data Science also helps consumers search for better goods,
especially in e-commerce sites based on the data-driven recommendation
system.

Disadvantages
Below are the disadvantages:
The disadvantages are generally when data science is used for customer profiling and
infringement of customer privacy. Their information, such as transactions, purchases,
and subscriptions, is visible to their parent companies. The information obtained using
data science can be used against a certain group, individual, country, or community.

Data Science Application in Real World Scenario.
Nowadays, absolutely everything is based on data. It doesn’t really matter if you sell
shoes, run a marketing agency or produce bicycle tires. Data is essential for you.
You use it all the time. Data helps you in almost everything you do. From searching
on Google, through hiring new workers up to creating financial reports. It really is all
about data.
How does data science usage help your business?

Make better decisions
Firstly, with data science, you can make better decisions. Why? Because they are made not on someone’s
opinion but on a much more reliable source. Only data science and machine learning systems can analyze
millions of bytes of the given data within seconds.
Increasing sales
Secondly, the usage of data science helps in rising sales. Machine learning systems can explore historic data, make
comparisons and analysis of the market and, on that basis, make recommendations of how, when, and where your
product or service will sell best. What’s more, data science can help you in improving accuracy in reaching your target
audience.

Google Analytics – one of the data science examples
Data analytical systems, such as Google Analytics, deliver you accurate data about who
visited your website or e-commerce, when, from where what was he or she interested in,
and many more. If you have been using Google Analytics already, you know how powerful
a tool it is. It helps you to suit your target audience’s needs, and that is done by modifying
your advertisements, your website’s layout, or even offers too!
Using data science may cause your company to implement some changes, because with
the data-based solution probably you will see some new and unexpected possibilities. But
the results of implementing the data-based strategy can also be unexpectedly good!

2. Supply chain optimization in the logistics industry
While we talk about optimizing the supply chain, we go straight to the second example of data science
projects: it is also of huge importance in the logistics industry.
● Optimization algorithms are able to shorten the delivery time and select the optimal route for the
vehicles – thus reducing operating costs and speeding up the work!
● Data science can also optimize the warehouse sector. Also, it saves time, space, and resources while
reducing errors in managing the warehouse.

3. Customer analytics in the retail industry
Another data science example is customer analytics in the retail industry. Let’s take a closer look at the
advantages of this data science project example.
● Data science apps can manage promotions and discounts actually in real-time. In addition, it could help
in selling out old products or creating interest in the new products.
● Data science can scan the whole social media network in order to forecast what products will be in-
demand in the near future and promote exactly the same products to the market.

4. Recommendation systems in marketing & advertising
For marketers, it is very valuable to analyze user behavior on their websites. Therefore, using data science in
marketing, companies can determine:
● what are the tastes and preferences of the customers
● what kind of knowledge or help they seek
● what are they interested in
● what do they want to buy
● how much do they want to pay for it.

5. Credit scoring for financial institutions (one of the most popular Data Science examples
Application)
The banking sector is the next data science project example.And one of the biggest problems of this
sector is NPA – Non-Performing Assets. These are the loans that haven’t been settled for at least 90
days. After that period loans become NPA . And the problem is very serious. As EBF (The European
Banking Federation) informs, on average, 3.74% of all worldwide loans are NPAs.
[1] How data science can help to solve this problem?
● Based on an analysis of the given customer’s banking history, data science mechanisms can
estimate loan debtor’s creditworthiness and predict which loans can in the future become NPLs.
So one of the usages of data science in the banking industry is risk management.

● Data science allows financial institutions to identify the most suspicious operations and pass them for a
deeper analysis. Moreover, it helps to detect illegal transactions that would be very difficult to detect for
employees manually.

6. Sales analytics
Sales – exactly what every company is about. Sales representatives have a very tough job. Hundreds of
phone calls, meetings, follow-ups, offers, and presentations. Always in a rush. Every day. But data science
usage can help them as well! Consider two data science examples.
● The data science algorithms can help sales representatives in deciding between products or services
eligible to suggest to the potential client. Or they can indicate what discount would be reasonable. Data
science in company is fast, accurate and irreplaceable support.
● Data science can indicate which prospects sales representatives should focus on, which prospects
have the biggest chance to close the deal. There’s plenty of options.

7. Predictive analytics in healthcare
Another important data science example – predictive analytics in healthcare.The predictive model
analyzes historical data, learns from it, identifies trends and then generates accurate predictions
based on those tendencies. So, data science in healthcare helps hospitals to:
● finds various correlations and associations of symptoms
● improve patient care
● improve supply chain efficiency and pharmaceutical logistics
● predict deteriorating patient health, provide preventive measures, and initiate therapy at an early
stage.

8. Weather predictions in agriculture sector
The last data science example is weather predictions in the agriculture sector. Nowadays, data science is changing the way
farmers and agriculture professionals make decisions.
Weather has a significant impact on agricultural production, affecting crop growth, development, and productivity. Using
data science in agriculture sector, farmers can get such elements of agriculture weather prediction as [2]:
● The amount and type of sky coverage
● Snowfall and precipitation (Rain)
● Maximum, minimum and dew point temperatures
● Humidity relative
● The direction and speed of the wind
● Low pressure areas

Becoming Data Scientist
Becoming a Data Scientists is an exciting path, but you cannot learn data science
within one year or six months—instead, it’s a lifetime process that you have to
follow with proper dedication and hard work.
STEP 1: Choose A Programming Language (Python / R)
The first step while starting the Data Science Journey is to get familiar with a programming language. Between the two,
Python is the most preferred coding language and is adopted by most Data Scientists. It is easy to understand, versatile,
and supports various in-built libraries such as Numpy, Pandas, MatplotLib, Seaborn, Scipy, and many more.

1. FreeCodeCamp’s Python Tutorial (Recommended)
2. Kaggle’s Python Course
3. Krish Naik’s Python Tutorial (Recommended)
4. Udemy’s Python for Data Science and Machine Learning Bootcamp
5. Coursera Python Course
NOTE: While learning Python, one should know essential Python variables, data types, OOPs
concepts, Numpy, Pandas, Matplotlib, and Seaborn.

STEP 2. Statistics
For becoming a Data Scientist, having knowledge of statistics and probability is as essential as having salt in food. Knowing
them will help the data scientists interpret large data sets, get insights from them, and analyze them better.
1. Krish Naik’s Statistics Playlist (Recommended)
2. Coursera Statistics Course
3. Khan Academy Statistics And Probability Course
4. FreeCodeCamp Statistics Course (Recommended)
NOTE: Statistics provides the ideas about Mean, Median, Mode, Range, Variance, Standard Deviation, Graphs or
Plotting, Populations, and Samples.

STEP 3: Learn SQL
Structured Query Language (SQL) is used for extracting and communicating with large databases. One should focus on
understanding the different types of normalization, writing nested queries, using co-related questions, group-by, performing
join operations, etc., on the data and extract in raw format. This data will then further be cleaned either in Microsoft Excel or
by using Python libraries.
1. Freecodecamp SQL (Recommended)
2. Intro To SQL By Kaggle (Recommended)
3. Advanced SQL By kaggle
4. Edureka’s SQL Playlist
NOTE: In SQL, one should know about creating tables, inserting data, updating data, deleting data, and performing
some basic query operations.

STEP 4. Data Cleaning
When a Data Scientist is given a project, the majority of the time goes into cleaning the data set, removing unwanted
values, handling missing values. It can be achieved by using some inbuilt python libraries like Pandas and Numpy.
One should also know how to manipulate data using Microsoft Excel.
1. Blog — Cleaning Data Using Python (Recommended)
2. Edureka’s Microsoft Excel Course
3. Learning Pandas By Kaggle (Recommended)
NOTE: In Microsoft Excel, you should know basic data filtering or sorting, Functions or Formulas, Vlookup, Pivot table
and charts, and Tables, etc.

STEP 5: Exploratory Data Analysis
Exploratory data analysis is the essential part when talking about data science. The data scientist has many tasks, including
finding data patterns, analyzing data, finding the appropriate trends in the data and obtaining valuable insights, etc., from
them with the help of various graphical and statistical methods, including:
A) Data Analysis using Pandas and Numpy
B) Data Manipulation
C) Data Visualization
1. Intro To EDA By Code Heroku’s (Recommended)
2. Blog — Performing EDA on Iris Data Set (Recommended)
3. Coursera Course On EDA, Statistics, Probability

STEP 6: Learn Machine Learning Algorithms
According to Google, “Machine learning is a method of data analysis that automates analytical model building. It is a branch
of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with
minimal human intervention.”
It is the most crucial step in a life cycle of a data scientist where one has to build various models using machine learning
algorithms and should be able to predict and come with the most optimum solution to solve any problem.
1. Machine Learning By Andrew NG (Recommended)
2. Deep Learning By Krish Naik
3. Intro To ML By Kaggle (Recommended)
4. Machine Learning By Krish Naik (Recommended)
5. Coursera Deep Learning Specialization

Step 7: Practice On Analytics Vidhya and Kaggle
After acquiring the basics of Data Science, now it’s time to get hands-on experience in its part. There are many online
platforms, like Kaggle and Analytics Vidhya, that can provide you with hands-on experience with both beginner and
advanced level data sets. They can help you to understand various machine learning algorithms, different analyzing
techniques, etc.
You can follow the below approach to know how effectively you can use these platforms.
1. You can start by first downloading the datasets and analyzing the data, and implementing all the different
techniques you have learned.
2. Next, you can check on other people’s notebooks and understand how they have solved a particular problem or
gained insights from the data. (This method will certainly make you more confident and help to improve your
knowledge.)