In this week’s article, I will share the roadmap I'll be following along with some useful resources I have gathered this month. And I'd love to know what you are relying on for your learning.
Who is a Data Analyst ? 🧐
He is a person responsible for collecting, analyzing, and interpreting large amounts of data to help inform business decisions. They use statistical methods and tools to turn raw data into actionable insights, which can help organizations make data-driven decisions. Data analysts typically work in fields such as business, finance, and healthcare...
What is the difference between a Data Analyst and a Data Scientist ? 📈 📉
Data analysts and data scientists both work with data, but their roles have some key differences:
Focus: Data analysts primarily focus on analyzing data to support decision making, while data scientists have a broader focus that includes designing and building predictive models, running experiments, and creating machine learning algorithms.
Skillset: Data analysts typically have strong skills in data visualization, reporting, and statistics, while data scientists have a wider skill set that includes programming, machine learning, and deep learning.
Scope: Data analysts typically work on smaller, more focused projects, while data scientists often work on more complex, large-scale projects that require a combination of technical and business skills.
Problem-solving: Data analysts typically solve problems that have defined solutions, while data scientists approach problems as open-ended research and development projects. To summarize, data analysts provide insights and support for decision making, while data scientists drive innovation by discovering and solving complex problems with data.
Career paths for a Data Analyst 👨💻
a data analyst can become a data scientist. While the roles of data analyst and data scientist have some differences, they also have some overlap, and many data analysts transition into data science positions over time. To make this transition, a data analyst may need to develop new skills and gain experience in areas such as machine learning, programming, and big data technologies. They may also need to demonstrate an understanding of the broader landscape of data science and an ability to work on complex, large-scale projects. In many organizations, the transition from data analyst to data scientist can occur through on-the-job learning and experience, as well as formal training and education. Ultimately, becoming a data scientist requires a combination of technical skills, problem-solving ability, and business knowledge.
On the other hand, there are several paths that one can take within this field. Here are a few of the most common career paths for data analysts:
Business Intelligence: Work as a business intelligence analyst, focusing on providing insights and recommendations to support decision-making processes within an organization.
Financial Analysis: Work in the finance industry, analyzing data to support investment decisions and helping organizations manage risk. Marketing Analytics: Help companies understand customer behavior and preferences through the analysis of market data and provide insights to inform marketing strategies.
Healthcare Analytics: Analyze health data to support medical research and improve patient outcomes.
Supply Chain Analytics: Use data to optimize and improve supply chain processes, such as inventory management, shipping and transportation, and manufacturing.
Advanced Analytics: Move into more specialized roles that involve working with machine learning algorithms and predictive modeling to solve complex problems.
Management: With experience, a data analyst can move into management roles such as leading teams of data analysts or overseeing projects.
What do you Need to Learn to Break into the Data Analytics World 💻📶✨
Breaking into the data analytics world requires a combination of technical skills, business knowledge, and problem-solving ability. Here are some of the key skills and areas of knowledge that are important for a data analyst to learn:
Statistics and Data Analysis: Understanding of statistical concepts, such as probability, hypothesis testing, and regression analysis, and the ability to apply these concepts to real-world data problems.
Data Visualization: Proficiency in using data visualization tools, such as Tableau and PowerBI, to communicate insights and findings effectively. Database Management: Knowledge of database management systems, such as SQL, and the ability to extract and manipulate data from databases.
Programming: Proficiency in at least one programming language, such as Python or R, to automate data analysis tasks and build predictive models. Business Acumen: Understanding of business operations, finance, and management to apply data analysis to real-world business problems if you want to work in BI.
Machine Learning: Knowledge of machine learning algorithms, such as linear regression, decision trees, and neural networks, and the ability to build predictive models; this is mostly for data scientists.
Communication Skills: Excellent written and verbal communication skills to effectively present data insights and findings to stakeholders. Simplifying the meaning requires good communication skills, that is so important and makes the person you are working with even if they don’t have a prior knowledge of the technical things be at ease.
In addition to these technical skills, a data analyst should also have a strong problem-solving ability, Logical thinking, Curiosity about data, and attention to details, an aptitude for learning and continuous improvement, and the ability to work in a team.
Roadmaps 🎢
Starting off with CodeBasics Roadmap, this is the introductory video, and the pdf that explains the learning rate each week is This. Let me tell you this guy has a huge impact on my coding skills. I took the python course he has on Youtube and it is a masterpiece. This is a three month roadmap, with 4 hours study per day. 3 hours for technical skills and 1 hour for soft skills. And let me tell you soft skills are as important as the technical ones. For that reason I started taking my LinkedIn profile very seriously , Oh! make sure to follow me there, This is the link to my profile.
One of the greatest Youtube channels is Alex the Analyst , he also will upload very soon a roadmap to become a data analyst as a playlist on his channel. And you can get a certificate and the end of the course.
To learn and practice Python you can check Python Doctor or HERE. Take courses on LinkedIn Learning, DataCamp, Free CodeCamp, Udemy, There are some courses on Harvard website that you can take for free. Once you are done, make sure to do some projects to build a good portfolio and showcase your skills.
‘ It’s what you learn after you know it all that counts.’ John Wooden