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DScience - 11 July 2024 Skills, Tools, Technologies, Practitioners This is the first of a number of posts dealing with my career transition activities into Data Science that I have decided to post here. I am hoping my continuing lessons and learning activities can be of benefit to others considering such a change. Additionally, passing along locations of pitfalls might well be of use to others. And I am always looking for considered advice and experiences! The last several years has seen an explosion in the data science field. What kind of skills does this field require to be successful? Data scientists need a diverse set of skills, tools, and aptitude in technologies to effectively gather, process, analyze, and interpret large amounts of data. Here is a summary of those. 1. Exploratory Data Analysis (EDA) Techniques to understand data patterns and distributions 2. Data cleaning and preprocessing Before stored data can be used to draw conclusions, issues such as missing values in the columns of a dataset, data needing to be transformed and normalized must be addresssed. 3. Data Scientests must be conversant in statistical testing. Chi-squared tests, t-tests, z-scores, etc. Fully understanding hypothesis testing, p-values and confidence intervals is critical as well as regression testing issues and correlation identification. 4. Model selection and evaluation is generally the next block of skills that are needed (including R/Python). Different datasets and different goals of the analysis require model selection. 5. Predictive modeling is often needed in order to perform predictions with the model. This knowledge and abilities however must be linked to the current tools and technologies available. Advances in data science would not have been possible without advances in capabilities of tools and technologies. TOOLS AND TECHNOLOGIES Python, R, SQL, pandas, NumPy, scikit-learn, TensorFlow, MathPlotLib, Plotly, Tableau, Power BI, Hadoop, Spark, AWS, MS Azure, Pycharm, Jupyter Notebok are all “players” in what tools and technologies are involved with Data Science activities. SUMMARY Perhaps the most important skills, tools and technologies used in data science are: 1. Analytical skills. Every data science project involves the extraction of data; usually large amounts of data. The analysis of the data however, requires a very specific and not easily found set of skills. “What have I missed in the model generation? There just seems to be something that is missing in the analysis.” 2. Life-long and QUICK learner. Good data scientists are ALWAYS looking to improve their analytic skills. They are ALWAYS sharpening their statistical and mathematical skills. Next post: I will describe the Data Science certification program that I am in. Some people pursue advanced master degrees in Data Science. I am pursuing the certification route. Analytics Vidhya MIT Professional Education Booz Allen Hamilton Boeing Harvard University Steve Hodges

Diving Into The Heart of Data Science 🌐✨ Hello there, curious minds! Let's dive into the fascinating world of data science. Data science is the art and science of extracting meaningful insights and knowledge from structured and unstructured data. It involves a combination of statistical analysis, machine learning, and domain expertise to uncover patterns, make predictions, and drive informed decision-making.👩💻📈 In the realm of data science, several key elements play a crucial role. First and foremost, the vast amounts of data generated daily highlight the importance of data processing and management. This includes understanding the intricacies of databases, such as Relational Database Management Systems (RDBMS). These systems, like the backbone of data storage, enable efficient organization and retrieval of information, making them an indispensable part of the data science journey. Now, let's explore three compelling facts about data science. 🔍 Firstly, the sheer impact of data science in transforming industries and shaping our digital landscape is undeniable. From predicting market trends to optimizing business operations, data science has become a cornerstone of innovation. Secondly, the ability to harness the power of data empowers individuals to make informed decisions in various aspects of life, fostering a data-driven culture. Lastly, the interdisciplinary nature of data science opens doors for collaboration between diverse fields, creating a dynamic ecosystem of knowledge exchange. Feeling the curiosity spark? Let's embark on a journey of learning and exploration in Data Science!🚀 Join me in unraveling the mysteries, acquiring skills that shape the future. You can check out my learning progress in the first week with Digital Skola to learn about Introduction to Data Science. The beauty of learning data science lies in its accessibility. Anyone can start their exploration with tools like DBeaver or Beekeeper Studio. These user-friendly platforms provide a hands-on experience in querying databases, a fundamental skill in the data science toolkit. So, whether you're a seasoned professional or a curious beginner, the journey begins with a single step—learning the basics of data manipulation. In closing, remember that data science is not just a field; it's a mindset—a way of thinking that empowers individuals to uncover hidden truths and drive meaningful change. As you embark on your data science adventure, keep in mind this tagline: "In the language of data, every insight tells a story, and every story has the potential to reshape the future." So, let's decode, learn, and empower ourselves with the language of data!📊💡 #DigitalSkola #LearningProgressReview #DataScience

Demystifying Hashmaps: A Key to Efficient Data Storage The world of data structures holds a captivating allure, and recently, I've been delving deeper into the fascinating realm of hashmaps. While these might seem like complex concepts, understanding their power can significantly enhance your ability to manage and manipulate data within your projects. Hashmaps: A Balancing Act of Speed and Efficiency Imagine a bustling library overflowing with books. A traditional library catalog might organize books alphabetically, requiring you to potentially sift through numerous entries to find a specific title. Hashmaps, on the other hand, function like a brilliant librarian with an exceptional memory. They utilize a unique key-value pairing system to store and retrieve data with exceptional speed. Here's a glimpse into how Hashmaps work: Keys and Values: Each entry in a hashmap consists of a unique key and an associated value. The key acts as a quick identifier, similar to a book title in the library catalog analogy. Hashing Function: The key goes through a special function called a hashing function, which transforms it into a unique index within the hashmap. This index is like the specific location on a library shelf where the book resides. Direct Access: By using the hashed index, the hashmap can directly retrieve the corresponding value, significantly faster than searching through a long, unordered list. Unlocking Efficiency Across Projects: Hashmaps' ability to store and access data with exceptional speed makes them highly valuable in various programming domains: Data Caching: Frequently accessed data can be stored in a hashmap for swift retrieval, improving application performance. Object Properties: Hashmaps are often used to represent object properties in many programming languages, providing a flexible way to store and manage associated data. Unique Value Lookups: Hashmaps excel at quickly determining if a specific value exists within a dataset, a crucial operation in diverse scenarios. A Foundation for Further Exploration Understanding hashmaps lays a solid foundation for exploring other advanced data structures. Their efficient key-value pairing system is a cornerstone of many modern data management solutions. Are you passionate about mastering fundamental data structures? Share your experiences or any data structures you find particularly intriguing in the comments below! #Hashmaps #DataStructures #Algorithms #Programming This post showcases your interest in learning foundational computer science concepts. It demonstrates your understanding of a key data structure and its potential applications, making you a more valuable asset to any tech team. https://lnkd.in/d5Uw8mmA

🚀 Week 1 of My Data Science Journey: Exploring the Core Foundations! This week marks the beginning of my exciting journey into the world of data science. I've immersed myself in two fundamental topics: Introduction to Methodology in Data Science and Introduction to Data & Databases. Here’s a detailed overview of what I've learned so far: Starting with Introduction to Methodology in Data Science, I began by grasping the essence of data science and its significance in transforming raw data into valuable insights. I explored the diverse outcomes data science can achieve, from predictive models and data visualizations to actionable insights that drive decision-making. Identifying the key skills necessary for a successful data science career was enlightening. Programming in Python and R, understanding statistics, mastering machine learning, and visualizing data effectively are all essential. Learning the steps involved in data processing—collecting, cleaning, and preparing data for analysis—ensured I grasped the importance of data quality and consistency. Diving into techniques for discovering insights was fascinating. Analyzing data to uncover patterns and trends that can influence decisions highlighted the power of data science. I also gained an understanding of how to build predictive models using machine learning algorithms, which can forecast future outcomes based on historical data. Exploring structured methodologies like CRISP-DM (Cross-Industry Standard Process for Data Mining) provided a framework for tackling data science projects systematically. Learning how data science is applied in various business contexts demonstrated its real-world impact in solving problems, optimizing processes, and enhancing decision-making. Moreover, discovering the vast array of career opportunities in data science—from data analyst to data engineer to data scientist roles—was truly inspiring. In the Introduction to Data & Databases, I gained a foundational understanding of data, including its different types—structured, unstructured, and semi-structured—and their importance in the data science workflow. Learning about databases and their role in efficiently storing and organizing large volumes of data was insightful. I explored various Database Management Systems (DBMS) like MySQL, PostgreSQL. The importance of Entity-Relationship Diagrams (ERD) in designing and visualizing database structures and relationships became clear. Additionally, writing basic SQL queries to manipulate and retrieve data from relational databases was an essential skill I began to develop. Understanding Data Lifecycle Management (DLM) was crucial, as I learned about the processes and best practices for managing data throughout its lifecycle—from creation to deletion. This first week has been incredibly enlightening for my data science journey. Can't wait to dive deeper into more advanced topics. Excited for the next steps! 🚀📈 #DigitalSkola #LearningProgressReview #DataScience

Understanding different types of Data - Structured | Unstructured | Semi-structured. As data analysts, data scientists, and data engineers, we work on gathering, transforming, generating insights, and building predictive algorithms with data. Have you ever thought about different types of data like data types in other programming languages? For instance, in Python, we have int, float, str, list, etc. Knowing the types of data is crucial for data professionals as it helps in choosing the right storage solutions, data processing techniques, and analysis methods based on the nature of the data. Structured: - The most common type of data used is structured data. - It is also known as relational data. - Letters and numbers are organized into columns and rows for simplified search and processing. - It is stored in a Microsoft Excel table that is familiar to us. Larger scale data may be stored in a relational database, like an Azure SQL database. - Structured data is typically quantitative and stored in relational databases and data warehouses. - Structured Query Language (SQL) is used to query relational databases. - Some applications where relational data is used include customer relationship management, reservations, and inventory management systems. Unstructured: - Unstructured data doesn't have a predefined structure or format. - It is often more suitable for qualitative analysis and is stored in non-relational databases and data lakes. - Examples of this type of data are text documents, audio & video files, social media posts, and images which require advanced techniques like NLP or image recognition for analysis. Semi-structured: - It is not as organized as structured data and is not stored in a relational database. - Examples include XML files, JSON data, and data with key-value pairs. - It is often stored in NoSQL databases like MongoDB. Do follow VISWAVARDHAN REDDY GUNDAVARAPU ✅ #dataanalytics #dataanalysis #typesofdata 

#Day02 Data Engineering Learning Sharing Joins:- Unveiling the Power of Connections Join types in PySpark are like the compass that guides you through the vast sea of data. Each join type has its unique characteristics and strengths, let's explore some of the most common join types: 1.    INNER JOIN: The Matchmaker An INNER JOIN in PySpark brings together records with matching values from both dataframe, ensuring that you capture precisely the insights you need. It's like playing matchmaker for your data, forging connections that reveal hidden patterns and relationships. 2.    LEFT JOIN: Embracing Inclusivity With a LEFT JOIN, no record gets left behind. It grabs all records from the left dataframe and pairs them up with matching records from the right dataframe, ensuring inclusivity in your analysis. It's about embracing every aspect of your data and leveraging it to gain deeper insights. 3.    RIGHT JOIN: Flipping the Script The RIGHT JOIN flips the script, giving the right dataframe its moment to shine. By pulling in all its records and finding their perfect matches in the left dataframe, it ensures that every aspect of your data is accounted for, no matter which side it comes from. 4.    FULL OUTER JOIN: Celebrating Diversity Love knows no bounds with a FULL OUTER JOIN. It celebrates matches from both dataframe while gracefully handling solo records with NULL values, ensuring that every piece of data is included in your analysis. It's about embracing the diversity of your data and finding beauty in every connection. Optimization Techniques: Navigating the Waters with Ease In the vast sea of data, optimization techniques are like the sturdy ship that helps you navigate through rough waters with ease. These techniques ensure that your data analysis is not only accurate but also efficient. Let's explore some of the most powerful optimization techniques in PySpark: 1.    Broadcast Join: Sending Signals Across the Cluster A Broadcast Join is like sending out a signal to all nodes in your cluster, ensuring that everyone's on the same page for the join operation. It's perfect for harmonizing small lookup tables with their larger-than-life fact table counterparts, ensuring seamless data integration. 2.    Shuffle Hash Join: Making Magical Connections The Shuffle Hash Join is all about making magical connections between your data. By hashing join keys and redistributing data across the cluster, it ensures efficiency and accuracy in your analysis, even in the most challenging circumstances. 3.    Shuffle Sort Merge Join: Bringing Order to Chaos In the chaotic world of data analysis, the Shuffle Sort Merge Join brings order to the chaos. By sorting both Dataframe based on the join keys before seamlessly joining them together, it ensures a smooth and efficient process, no matter the size or complexity of your data. https://lnkd.in/dtsWYtF8

Setting priorities is a data science problem. It’s easy to say “set your priorities and stick to them.” But not only is that an oversimplification - it might also be bad advice. This challenge of setting priorities (and sticking with them) often boils down to “Resource Allocation” - a concept from the field of data science that’s been studied extensively over the last few decades. So let’s steal some tricks from data science to sort it out! (No Ph.D required! 😉) First, Identify Your Constraints: This is where linear programming comes into play. It's a method used by data scientists to find the best possible outcome within given constraints, like minimizing costs or maximizing efficiency. For you, this means understanding your limitations in terms of time, energy, and attention. By defining these boundaries clearly, you can optimize how you use these limited resources. If you’re a nerd like me, sketch out a formula to visualize how changes in one area impact another. Next up, Optimize Your Resource Allocation: Think of the Knapsack problem, a classic optimization puzzle that involves selecting the most valuable items without exceeding the weight limit of your knapsack. Apply this to your daily tasks by choosing activities that offer the greatest return on investment of your time and effort, ensuring you don’t 'overpack' your day. Finally, Adapt and Adjust with Dynamic Programming: Unlike linear programming that focuses on static scenarios, dynamic programming is used for making a sequence of interconnected decisions. It provides a structured approach to break down complex decision-making into simpler, manageable steps. What does this mean? Your plan for the week should be adaptive, changing as new priorities emerge and as tasks are completed. Your priorities on Monday probably look different by Friday - and that’s okay! Be flexible and adjust your plan when encountering new information or outcomes. Understand what your threshold is for allowing your priorities to change. While you don’t want priorities to be rigid, immovable objects…you also don’t want to reorganize them every time the wind blows. Keeping these data science principles in mind during your daily and weekly planning can give you a new perspective on strategically managing your resources. I’ve found that this approach not only helps me clarify what's most important, but also enhances my ability to achieve goals efficiently. That being said - human brains aren’t built for this type of thing. It’s a tough skillset to learn. Thankfully, you don’t have to go it alone. ☺️🤖 At Aneu, our Navigators are built for this exact type of support, and to empower you with science-based, data-driven personal development. Come design your own Navigator today at www.aneu.ai. #youngprofessional #personaldevelopment #priorities #datascience #goals #personalAI #newjob #aneu

Day 1 of #tech4dev Bootcamp3.2 The class was knowledge-packed as our trainer Clifford Frempong demystified Data Science. The goal of the class was just an introduction to Data Science. Data Science is an interdisciplinary field that uses scientific methods, Processes, algorithms, and Systems to extract knowlege and insights from Structured and unstructured data.Put, Data Science is Using data that could be structured like prices, weather, weight etc and unstructured data like images, audio, text etc to understand different things that concern the Organizations. The data in an organization is usually saying something about that organization so data science helps to understand or decode the message. Why do we need data science most especially now Due to advancement in technology companies are bombarded with a lot of data and it is in the interest of that company to harness the power of that data inorder to generate insights that will be used for decision making that will affect the company positively especially increase sales. Data science helps business to be more strategic in their marketing campaigns so that with lower budget they can achieve maximum result. Data science Teams in organizations consist of different people with different skillset. Data science Jobs consist mainly: 1. Data Engineeer who is responsible for collecting organization data from various sources such as database, Excel sheet, website etc and make it available to the right people and in its efficient form 2. Data Analyst whose job is to work on the data and build visualizations used for making informed decisions. 3.Machine learning Engineers who ensures that models developed are running smoothly and giving accurate results at the right time. Data is scattered in organizations so much so that they require this set of people to work on data. Different roles are emerging now days which are combination of different roles such as analytics engineer etc. your company may determine your responsibilities. We have five types of analysis which are: 1.Descriptive analysis: This type of analysis is to summarize, describe and understand data patterns,trends and distributions. 2.Diagnostic analysis: it uses data to understand factors behind past events,trends and outcomes. 3.Predictive analysis: it aims to help organizations make proactive decisions and provide insights into potential risks and opportunities. 4.Precriptive analysis: it aims at recommending actions in response to a given forecast to optimize desired outcomes. 5.Cognize analysis:cognitive analysis aims to help organizations extract insights and make predictions from complex and unstructured data sets that may be difficult to process using traditional methods. This just a summary of all I learn't. Thank you #tech4dev for giving me the opportunity to upskill in #dataanalytics and #visualization