1 / 11

Learn Data Science Big Data and Data Analytics Concepts and Applications

In the world of data science, the terms big data, data science, and data analytics are often mixed and confused. Understand their differences and roles in this detailed guide.

Priyanka225
Download Presentation

Learn Data Science Big Data and Data Analytics Concepts and Applications

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. LEARN DATA SCIENCE, BIG DATA AND DATA ANALYTICS CONCEPTSAND APPLICATIONS www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  2. Data Science is larger than one can imagine. It is the science and art of converting data into insights to drive innovations and boost business productivity. During the entire data science lifecycle, different processes and different components are involved that lead to the successful transformation of data from insights. The data science industry encompasses data, tools, mathematics, algorithms, analytics, visualization, and so on. This briefly describes the difference between data science, big data, and data analytics. Further in this guide, we will delve into these three big concepts and understand the differences in detail. OVERVIEW OF THE DATA SCIENCE INDUSTRY Before we get started, let us understand what the need is for asking this question. Well, because if you are looking to make a career in data science, then you need to be aware of the differences between these three big terms. The global data science platform market is expected to reach around $322.9 billion by 2026 (Markets and Markets). This huge growth will also increase the demand for skilled data science professionals, and so do the competition. Employers certainly will need professionals who at least know the clear difference between data science, big data, and data analytics. Therefore, if you want to grab the huge opportunities, around 11.5 million data science jobs by 2026 (US Bureau of Labor Statistics) to be specific, then read along and enhance your understanding of these main data science terms. DATA SCIENCE WHAT IS DATA SCIENCE As mentioned previously, data science refers to the technology used to extract insights from raw data. Using data science, organizations can identify patterns, trends, and insights that will help them make data-driven decisions. So, data science is the process that ultimately leads to innovations, efficient business operations, and enhanced customer experience. www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  3. DATA SCIENCE PLATFORM MARKET SIZE 2022 TO 2032 ($ BILLION) $501.03 502 451.8 $429.70 401.6 $368.84 351.4 $316.87 301.2 $272.46 $234.48 251 $201.96 200.8 $174.10 $150.22 150.6 $129.72 $112.12 100.4 50.2 0 2023 2025 2026 2027 2030 2031 2032 2022 2024 2028 2029 Source: www.precedenceresearch.com Data Science basically incorporates 3 major fields of study; mathematics and statistics, computer science or programming language, and business or domain knowledge.‘Data Science’ can be considered an umbrella term that contains the entire process of data collection, cleansing, preparation, and analysis to finally generate valuable and actionable insights. APPLICATIONS Data Science has wide applications across all industries. Here are some top uses of data science: Recommendation system Suggest relevant products, services, and shows to customers based on their behavior and preferences. Fraud detection Identify frauds in financial transaction to prevent losses Algorithmic trading Make automated trading decisions depending on market data Drug discovery Speed up new drug development also personalize treatment plans Anomaly detection In the field of cybersecurity abnormal user or network behavior can be detected to identify potential threats. www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  4. POPULAR DATA SCIENCE TOOLS Data Programming Languages Manipulation and Analysis Data Machine Learning Visualization BIG DATA WHAT IS BIG DATA Big data refers to really big data by which we mean millions and trillions of bytes of data.They are extremely large and complex which are difficult to manage and process using traditional data management tools or applications. www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  5. THERE ARE THREE IMPORTANT CHARACTERISTICS OF BIG DATA VARIETY VOLUME includes petabytes or even exabytes of data VELOCITY refers to the high speed at which data is generated and requires real-time processing means various formats of data including structured and unstructured data. TYPICALLY, FOLLOWING ARE THE MAIN SOURCES THAT CONTRIBUTE TO GENERATION OF BIG DATA Scientific data and more Social media Sensor data Financial transactions Web server logs BIG DATA REVENUE WORLDWIDE (By segment in Millions) Total revenue of Big Data market Service revenue Hardware revenue Software revenue 206 193 200 181 46 167 42 154 38 141 150 34 24 23 128 31 22 27 112 20 33 24 19 99 32 18 31 20 100 16 29 84 27 17 15 26 24 70 14 14 21 11 12 19 10 50 16 14 35 42 49 56 70 77 84 90 96 64 103 2017 2019 2023 2026 2027 2018 2020 2021 2022 2024 2025 Source- Market.us Scoop www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  6. APPLICATIONS OF BIG DATA Big data can be used for various applications like Gaining new subscribers or retaining existing customers in the telecommunication sectors Offering personalized services in the retail sector by analyzing big data of customer behaviors BIG DATA TOOLS These are some of the popular and widely used big-data tools: DATA ANALYTICS WHAT IS DATA ANALYTICS? Data Analytics is the process of analyzing huge volumes of data (big data) to extract meaningful information out of raw data. While ‘data science’ refers to the entire technology of converting data into insights through building models, and algorithms; data analytics refers to this particular section of analysis in the entire data sciencelifecycle. www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  7. DATA ANALYTICS MARKET SIZE, 2022 TO 2032 ($ BILLION) $393.35 400 360 320 $303.04 280 $233.65 240 200 $180.28 160 $139.21 $107.58 120 $83.21 $64.4 80 $49.88 $30 $38.67 40 0 2023 2025 2026 2027 2030 2031 2032 2022 2024 2028 2029 Source: www.precedenceresearch.com DATA ANALYTICS INVOLVES VARIOUS STEPS AND HERE ARE THE KEY COMPONENTS OF DATA ANALYTICS Data Data cleaning Data Data analysis Data collection transformation visualization THERE ARE BASICALLY 4 TYPES OF DATA ANALYTICS NAMELY Prescriptive analytics Predictive analytics Diagnostic analytics Descriptive analytics www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  8. APPLICATIONS OF DATA ANALYTICS Data analytics is widely used for following purposes: Predicting user behavior and identifying insider threats in cybersecurity Customer lifetime value: data analytics is used to predict the long-term value of customers Early detection of disease by identifying risk factors Predict machine failures through sensor data and minimizing breakdown and downtime Identify fraudulent health claims and reducing costs DATA ANALYTICS TOOLS Data analytics is widely used for following purposes: www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  9. RELATIONSHIP BETWEEN DATA SCIENCE, BIG DATA, AND DATA ANALYTICS Though these three things are different, they are very much interconnected with each other. Data science is a broad term that encompasses both big data and data analytics. Let us understand this clearly here. Big provides raw material for data science and fuels data analytics. Where data analytics helps to understand the trends and patterns in data and make effective data science models Data science finally assists in data-driven decision-making to boost business and enhance customer service. UNDERSTANDING THE ROLE OF DATA SCIENCE PROFESSIONALS IN THESE THREE SECTORS Data Science Data Analytics Big Data Professio- nals Data Analyst Data Scientist Big Data Engineer Build predictive models, generate insights Extract insights from big data Manage and process huge datasets Focus SQL, data Statistics, programming language, machine learning, data visualization Hadoop, Spark, SQL, cloud computing Skills visualization, Python, statistical analysis Python, machine learning libraries, statistical software Tools Hadoop, Spark, Hive Excel, BI Tools, SQL Handle huge amount of data right from ingestion to processing Prepare data, analyze data, and generate report Develop algorithms, build prototypes, and predictive models Role Generates insightful reports and visualization for Creates strong infrastructure for DDDM Drive innovation, create new products and service Business Impact www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  10. IN A NUTSHELL DATA SCIENTISTS FOCUS ON BUILDING MODELS AND PREDICT FUTURE TRENDS BIG DATA ENGINEERS HELP WITH MANAGEMENT OF HUGE DATASETS AND DATA ANALYST EXTRACT INSIGHTS FROM DATA TO ASSIST DECISION MAKERS MAKER DATA-DRIVEN DECISIONS CONCLUSION So, by now you must have understood the fine line of difference between data science, data analytics, and big data. Though they are separated from each other in their core function, they are highly interconnected and dependent on each other and are often used interchangeably. However, by understanding this difference, you will be able to clearly set your career goals and decide how you want to enter your data science career; as data scientists, big data specialist, or data analyst. The road ahead in data science is full of opportunities. Are you ready enough to grab those? www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  11. START YOUR DATA SCIENCE JOURNEY © Copyright 2024. United States Data Science Institute. All Rights Reserved

More Related