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INTRO TO
DATA
SCIENCE
UMESHCHANDRA
INTRODUCTIONTO DATA SCIENCE
 Data Science is one amongst the fastest-growing, difficult and high paying jobs of this
decade. So, the question is what is data science? data science is an interdisciplinary
field (it consists of more than one branch of study) that uses statistics, computer
science and machine learning algorithms to gain insights from both structured and
unstructured data. According to ‘Economic Times’ India has seen more than 400
percent rise in demand for data science professionals across varied industry sectors
at a time when the supply of such talent witness slow growth.
ABOUT THIS COURSE
APPLICATIONS
1. Marketing
 There is a huge scope in marketing, for example, Improved Pricing strategy Companies like Uber, e-commerce
companies can use data science-driven pricing which allows them to increase 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
 As we discussed the introduction to data science now we will go ahead with the application of data science uses in
the banking sector for fraud detection which can be helpful in reducing the Non-Performing Assets of banks.
4. Government Policies
 The Government can use data science to prepare better policies to cater better 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
In this topic of IntroductionTo Data Science, we also show you the advantages of Data Science. Some of them are as
follows:
 It helps America to induce insights from the historical information with its powerful tools.
 It helps to optimize the business, hire the right persons and generate more revenue as using data science helps
you to 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
 As we studied about the introduction to data science now we are going ahead with
the disadvantages of data science:
The disadvantages are generally when data science is used for customer profiling and
infringement of customer privacy, as their information, such as transactions,
purchases, and subscriptions, is visible their parent companies.The information
obtained mistreatment knowledge science is used against a precise cluster,
individual, country or community.
OUTCOMES
On fortunate completion of this unit a student ought to be ready to:
 analyze the role of data in organizations, including curation and management issues;
 apply basic tools for performing exploratory data analysis and visualization;
 apply basic tools for managing and processing big data;
 apply basic predictive modeling and data analysis methods;
 determine data storage and processing requirements for a data science project;
 identify data resources and standards.
THANKYOU
Please Contact us Here by attached following link.
 ExcelR Solutons : https://g.page/ExcelRSolutionsBanaglore?share
 Contact Us @ Behind Tata Motors, 49, 1st Cross, 27th Main, Old Madiwala, Jay Bheema Nagar, 1st Stage, BTM
Layout, Bengaluru, Karnataka 560068.
 Website : https://www.excelr.com/

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Data science course in bangalore

  • 2. INTRODUCTIONTO DATA SCIENCE  Data Science is one amongst the fastest-growing, difficult and high paying jobs of this decade. So, the question is what is data science? data science is an interdisciplinary field (it consists of more than one branch of study) that uses statistics, computer science and machine learning algorithms to gain insights from both structured and unstructured data. According to ‘Economic Times’ India has seen more than 400 percent rise in demand for data science professionals across varied industry sectors at a time when the supply of such talent witness slow growth.
  • 4. APPLICATIONS 1. Marketing  There is a huge scope in marketing, for example, Improved Pricing strategy Companies like Uber, e-commerce companies can use data science-driven pricing which allows them to increase 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  As we discussed the introduction to data science now we will go ahead with the application of data science uses in the banking sector for fraud detection which can be helpful in reducing the Non-Performing Assets of banks. 4. Government Policies  The Government can use data science to prepare better policies to cater better 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.
  • 5. ADVANTAGES In this topic of IntroductionTo Data Science, we also show you the advantages of Data Science. Some of them are as follows:  It helps America to induce insights from the historical information with its powerful tools.  It helps to optimize the business, hire the right persons and generate more revenue as using data science helps you to 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.
  • 6. DISADVANTAGES  As we studied about the introduction to data science now we are going ahead with the disadvantages of data science: The disadvantages are generally when data science is used for customer profiling and infringement of customer privacy, as their information, such as transactions, purchases, and subscriptions, is visible their parent companies.The information obtained mistreatment knowledge science is used against a precise cluster, individual, country or community.
  • 7. OUTCOMES On fortunate completion of this unit a student ought to be ready to:  analyze the role of data in organizations, including curation and management issues;  apply basic tools for performing exploratory data analysis and visualization;  apply basic tools for managing and processing big data;  apply basic predictive modeling and data analysis methods;  determine data storage and processing requirements for a data science project;  identify data resources and standards.
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