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A BEGINNER’S GUIDE TO AN INCREDIBLE TECHNOLOGY- DATA SCIENCE

Data science is the sheer skill to gain competence in making sense of all the data pools that are being generated by organizations worldwide. From being the most promising and the hottest jobs in the world in 2023 global rankings by the World Economic Forum, you are sure to gain as a certified data scientist in the years to follow as well.<br>This has marked a clear beginning pathway for many who wish to explore the industry from massive career gains. Data science technology is being deployed and consumed in numerous ways. Glassdoor, Indeed, and many such reputed international employment portals

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A BEGINNER’S GUIDE TO AN INCREDIBLE TECHNOLOGY- DATA SCIENCE

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  1. A BEGINNER’S GUIDE TO AN INCREDIBLE TECHNOLOGY: www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  2. WHEN WE HAVE ALL DATA ONLINE IT WILL BE GREAT FOR HUMANITY. IT IS A PREREQUISITE TO SOLVING MANY PROBLEMS THAT HUMANKIND FACES.” - Robert Cailliau Informatics Engineer and Computer Scientist who helped to develop the World Wide Web Data Science refers to the art of drawing insights from Raw Data that assists Business leaders and Decision-makers in data-driven decision-making. For all the Students and Young Professionals, curious about what is data science and what’s the career prospects in this industry, this complete beginner’s guide to data science will answer all your queries. So, let’s explore the vast world of Data Science. INTRODUCTION TO DATA SCIENCE Data Science is a multidisciplinary field and consists of various fields of expertise including computer science, mathematics & statistics, and domain knowledge to efficiently extract meaningful insights from data. According to a report by IBM, 90% of organizations have reported an increase in usage of data science technology for their business operations in the past year. This increase in the use of data science can be directly attributed to the increase in the volume of data. The amount of data generated daily is growing at an astounding rate and it is expected to reach 175 zettabytes by 2025, predicts IDC. Be it social media interaction, financial transactions, medical records, or scientific research, data holds immense value for organizations to derive insights that can potentially revolutionize all industries, and transform the way we live, work, and make decisions. DATA SCIENCE WORKFLOW Here are the common steps followed in any data science project’s workflow. PROBLEM STATEMENT AND DATA COLLECTION The data science journey begins by identifying the particular problem the organizations want to solve with the help of data and data science. Then data science professionals start their jobs including data engineers and data scientists finding the relevant source of data. data can be collected through internal databases, external APIs, web scrapping, physical documents, etc. STEP 01 www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  3. EXPLORATORY DATA ANALYSIS (EDA) EDA is all about knowing the data. in this step, data science professionals use statistical techniques, visualizations like histograms and scatter plots, and other exploratory techniques to find the patterns, trends, and relationships in their data. STEP 02 STEP 03 DATA CLEANING AND PRE-PROCESSING Often the data collected from real-world situations are messed up. The datasets can have missing values, errors, or incorrect values. It needs cleaning and preprocessing before it is sent for analysis. STEP 04 DATA MODELING AND MACHINE LEARNING Data scientists use machine learning algorithms to learn from data and make predictions. The three main categories of machine learning are supervised learning, unsupervised learning, and reinforcement learning. MODEL EVALUATION AND DEPLOYMENT Once the data science model is ready, they are continuously evaluated and fine-tuned for maximum performance using metrics like accuracy, precision, recall, etc. This ensures the model is reliable before deploying for real-world applications. STEP 05 TOP JOB ROLES IN DATA SCIENCE Some of the most popular job roles in the data science industry include: Database Administrator Data Engineer Data Scientist/ Senior Data Scientists Chief Data Officer Data Analyst Machine Learning Engineer Machine Learning Scientist Business Intelligence Analyst Data Visualization/ Data Storytelling Specialist Data and Analytics Manager Data Architect Data Quality Manager www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  4. SALARIES OF IN-DEMAND DATA SCIENCE JOBS Annual Average Salary (in U.S.) Job role $155,263 Data Scientist $128,457 Machine Learning Engineer $153,065 Machine Learning Scientist $156,689 Enterprise Architect $183,037 Data Architect $121,919 Data Engineer $136,808 Business Intelligence Developer $76,809 Data Analyst $91,361 Statistician $145,670 Applications Architect Source: Glassdoor POPULAR AND MOST WIDELY USED DATA SCIENCE TOOLS A P A C H E Data Collection and Data- Ingestion Data Cleaning and Mining Data Exploration and Visualization Data Analysis Other Tools www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  5. CATEGORY TOOLS DESCRIPTION Dominant language; readable syntax, extensive libraries (NumPy, Pandas, Matplotlib) Python Programming Languages Popular alternative; strong in statistics and data visualization R Powerful library for data cleaning, transformation, and analysis Pandas OpenRefine (Google Refine) Data Wrangling and Manipulation A user-friendly tool for cleaning and transforming messy data Interactive platform for visual data wrangling Trifacta Wrangler Structured Query Language for relational databases SQL (MySQL, PostgreSQL) NoSQL Databases (MongoDB, Cassandra) Data Storage and Management Flexible databases for unstructured or semi-structured data Hadoop Ecosystem (HDFS, Spark) Scalable framework for storing and processing large datasets Comprehensive library for building and deploying various machine learning models Machine Learning Scikit-learn www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  6. CATEGORY TOOLS DESCRIPTION Open-source framework for numerical computation, deep learning, and large-scale machine learning TensorFlow Machine Learning Another popular deep-learning framework with dynamic computational graphs PyTorch Versatile library for creating various plots and charts Matplotlib Built on top of Matplotlib; a high-level interface for statistical graphics Seaborn Data Visualization Powerful visual analytics platform for interactive dashboards and data exploration Tableau Business intelligence tool from Microsoft for data visualization and reporting Power BI Amazon Web Services (AWS) Cloud platform offering various data science services (SageMaker, Elastic Compute Cloud) Cloud platform with data science tools like Azure Machine Learning and Azure Databricks Cloud Computing Microsoft Azure Google Cloud Platform (GCP) Cloud platform offering data science services including BigQuery and Vertex AI www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  7. BENEFITS OF DATA SCIENCE Data science can help businesses in numerous ways. Some of the notable benefits of incorporating data science into business include: Better Data-driven decision-making as it is backed by Improved efficiency as Data Science helps to Automate tasks, Optimize processes, and Reduce costs. Better Customer Experience by personalizing interactions, predicting needs, and boosting satisfaction Assist in innovation as Data Science can easily discover hidden patterns. It leads to the Development of New and Innovative products. Prevents risk through Predictive Analytics techniques and assists in identifying potential issues in all industries. APPLICATIONS OF DATA SCIENCE Data science isn’t limited to only a few specific sectors. Now organizations from every industry are using it to maximize their business operations. Here are the top applications of data science across various industries FINANCE HEALTHCARE Fraud detection, credit risk assessment, algorithmic trading, personalized financial products Personalized medicine, disease prediction, drug discovery, medical imaging analysis RETAIL MANUFACTURING Inventory management, demand forecasting, product recommendation, customer segmentation Predictive maintenance, quality control, process optimization, supply chain management MARKETING MEDIA & ENTERTAINMENT Customer segmentation, targeted advertising, campaign optimization, social media analytics Content recommendation, personalized advertising, audience segmentation, content creation www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  8. GOVERNMENT TRANSPORTATION Fraudulent tax detection, crime prediction, resource allocation, public health monitoring Route optimization, traffic prediction, demand forecasting, self-driving car development SPORTS Player performance analysis, injury prediction, game strategy creation, optimizing training regimens CAREER IN DATA SCIENCE: ROADMAP EDUCATION REQUIREMENTS OF DATA SCIENCE JOBS Data Scientist Machine Learning Engineer Machine Learning Scientist Applications Architect Enterprises Architect Data Architect Infrastructure Architect Data Engineer Business Intellegence Developer Statistician Data Analyst 10% 20% 30% 40% 50% 60% 70% 80% 90% Bachelor’s Degree Associate’s Degree Ph.D or Professional Degree Master’s Degree Source: Lightcast™ Analyst, 2023 www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  9. To get started with your data science career, you can follow this simple roadmap: EDUCATIONAL FOUNDATION 1 Bachelor's in computer science, information technology, maths, science, or related field Master’s in data science, data analytics, statistics, etc. GAIN RELEVANT DATA SCIENCE SKILLS AND KNOWLEDGE 2 Programming language Data analytics and visualization skills Soft skills are also important to consider VALIDATE YOUR EXPERTISE WITH TOP DATA SCIENCE CERTIFICATIONS CERTIFICATE 3 Enroll in data science certification programs Attend boot camp Browse free and paid certification courses BUILD A STRONG PORTFOLIO OF REAL-WORLD DATA SCIENCE PROJECTS 4 Get entry-level data science jobs Join internship Contribute to open-source projects Participate in a data science competition START JOB SEARCH 5 Network with other professionals in this field Stay active in the data science community and LinkedIn Reach out to employers Customize resume specific to job profiles Following these simple steps can help you get started with your data science career. www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

  10. CONCLUSION Data science is an incredible field that is growing rapidly. As more and more organizations seek to leverage the power of data science, the demand for data science professionals will soar high in the coming years. It is therefore recommended that you must enroll in the best data science certification programs, learn the latest data science skills, empower yourself with top trends and technologies in the world of data science, and ace this career path. www.usdsi.org www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved © Copyright 2024. United States Data Science Institute. All Rights Reserved

  11. BECOME A CERTIFIED DATA SCIENCE EXPERT WITH © Copyright 2024. United States Data Science Institute. All Rights Reserved

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