TechSid blogss

   👉Part 3 :  Types of Data and Data Challenges👈 Welcome back to the fourth installment of our Beginner's Guide to Data Science blog series! In the previous parts, we covered the basics of data science, the data science process, essential skills, and key tools and technologies. In this part, we will explore the different types of data that data scientists work with and the challenges involved in handling them. Structured Data: Structured data refers to data that is organized in a predefined manner, typically in tabular form with rows and columns. This data is commonly found in relational databases, Excel spreadsheets, and CSV files. Data scientists frequently work with structured data because it's easy to query and analyze using SQL and other data manipulation tools. However, challenges may arise when dealing with missing values, data inconsistencies, and data quality issues. Unstructured Data: Unstructured data refers to data that does not have a predefined format or...

  👉 Part 2: Beginner's Guide to Data Science Blog👈 Welcome back to the second part of our Beginner's Guide to Data Science Blog! In this section, we will delve deeper into data science concepts and explore various tools and techniques that can enhance your understanding and proficiency in this exciting field. Exploring Data Science Tools: Data science involves the use of various tools and programming languages to analyze and visualize data. Some popular tools used in data science include: 5.1 Python: Python is a versatile programming language and a go-to choice for data scientists due to its extensive libraries like NumPy, Pandas, Matplotlib, and SciPy. Python 5.2 R: R is another programming language specifically designed for statistical computing and graphics. It is widely used in academia and research settings. R Programming 5.3 Jupyter Notebooks: Jupyter Notebooks provide an interactive environment for data analysis, visualization, and storytelling. They allow you to c...

                                                          👉  Part 1 in Beginner's Guide 👈  Data science is an interdisciplinary field that involves the extraction of knowledge and insights from vast and complex datasets. It combines various techniques from statistics, computer science, and domain knowledge to uncover patterns, trends, and correlations in data, with the ultimate goal of making data-driven decisions.       Block Diagram       In brief, data science encompasses the following key steps: Data Collection: Gathering and compiling relevant data from various sources, which can be structured (e.g., databases, spreadsheets) or unstructured (e.g., text, images). Data Cleaning and Preprocessing: Ensuring the data is accurate, compl...