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Data plays a crucial role in driving informed decision-making and enabling ai-based applications. As a result, there is a growing demand for skilled data professionals across various industries. If you're new to data science, this extensive collection of guides is designed to help you develop the essential skills needed to extract insights from large amounts of data.
Link: 7 Steps to Master SQL for Data Science
It is a step-by-step approach to mastering SQL, covering the basics of SQL commands, aggregations, groupings, sorts, joins, subqueries, and window functions.
The guide also highlights the importance of using SQL to solve real-world business problems by translating requirements into technical analysis. To practice and prepare for data science interviews, he recommends practicing SQL through online platforms like HackerRank and PGExercises.
Link: 7 Steps to Master Python for Data Science
This guide provides a step-by-step roadmap for learning to program in Python and developing the skills needed for a career in data science and analytics. Start by learning the fundamentals of Python through online courses and coding challenges. Then, it covers Python libraries for data analysis, machine learning, and web scraping.
The career guide highlights the importance of practicing coding through projects and creating an online portfolio to showcase your skills. It also offers free and paid resource recommendations for each step.
Link: 7 Steps to Master Data Preprocessing and Cleansing Techniques
A step-by-step guide to mastering data preprocessing and cleansing techniques, which is an essential part of any data science project. The guide covers several topics, including exploratory data analysis, handling missing values, handling duplicates and outliers, categorical feature encoding, splitting data into training and test sets, feature scaling, and Treatment of imbalanced data in classification problems.
You will learn the importance of understanding the problem statement and data with the help of example codes for various preprocessing tasks using Python libraries such as Pandas and scikit-learn.
Link: 7 steps to master data management with Pandas and Python
It is a comprehensive learning path to master wrangling data with pandas. The guide covers prerequisites such as learning the fundamentals of Python, SQL and web scraping, followed by steps to load data from various sources, select and filter data frames, explore and clean data sets, perform transformations and aggregations, join data frames and create pivot tables. Finally, he suggests creating an interactive data dashboard using Streamlit to showcase data analysis skills and build a pipeline, essential for aspiring data analysts looking for job opportunities.
Link: 7 Steps to Master Exploratory Data Analysis
The guide outlines the 7 key steps to perform effective exploratory data analysis (EDA) using Python. These steps include collecting data, generating statistical summaries, preparing data through cleaning and transformations, visualizing data to identify patterns and outliers, performing univariate, bivariate and multivariate analyzes of variables, analyzing data of time series and the treatment of missing values and outliers. EDA is a crucial phase in data analysis, allowing professionals to understand the quality, structure and relationships of data, ensuring accurate and deep analysis in subsequent stages.
To begin your journey in data science, it is recommended to start by mastering SQL. This will allow you to work efficiently with databases. Once you are comfortable with SQL, you can dive into Python programming, which comes with powerful libraries for data analysis. Learning essential techniques like data cleansing is important as it will help you maintain high-quality data sets.
Then, gain experience manipulating data with Pandas to reshape and prepare your data. The most important thing is to master exploratory data analysis to deeply understand data sets and uncover insights.
After following these guidelines, the next step is to work on a project and gain experience. You can start with a simple project and then move on to more complex ones. Write about it on Medium and learn about the latest techniques to improve your skills.
Abid Ali Awan (@1abidaliawan) is a certified professional data scientist who loves building machine learning models. Currently, he focuses on content creation and writing technical blogs on data science and machine learning technologies. Abid has a master's degree in technology management and a bachelor's degree in telecommunications engineering. His vision is to build an artificial intelligence product using a graph neural network for students struggling with mental illness.