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Become A Data Analyst With This Free Learning Path

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If you want to become a data analyst, this learning path is for you. Whether you’re new to the world of data analysis or have been working with it for years, this path will help you start your journey toward becoming a successful analyst. The skills covered here are essential for aspiring data scientists, business analysts, or anyone who wants to get more out of their analysis workday!

Data Analysis Fundamentals

Data analysis is collecting, organizing, and analyzing data to extract information. It involves using one or more methods to determine relationships between variables and predict future events. This can be done manually or by computerized systems like predictive analytics software or predictive modeling tools.

Data scientists are experts at extracting insights from data sets collected and cleaned before they begin their analysis process; they use statistical techniques such as descriptives, estimation, hypothesis testing, regression analysis, and machine learning algorithms. They also analyze complex data sets using columnar storage formats because these types of files allow faster access than text files when searching through large volumes of records at once (such as 10 TB worth).

Data analysts work closely with business leaders who need more detailed information about how consumers behave within different contexts. For example, shopping experiences (online versus offline), interactions with brand reputation versus automated customer service, channels like chatbots versus phone calls, etcetera. This will help predict what will happen next so businesses can keep up with consumer trends effectively without unnecessarily wasting too much money!

Learning the SQL Query Basics

You can use SQL queries to retrieve data from your databases. SQL is a structured query language, a standard language for managing data in databases. There are two types of queries: SELECT statements and INSERT, UPDATE and DELETE (INSERT, UPDATE and DELETE) statements. In this module on SQL Queries, you will learn how to write simple queries using the SELECT statement followed by the WHERE clause, ORDER BY clause; GROUP BY clause; HAVING clause, and DISTINCT clause. You will also learn about sub-queries and joins and how to use them in SELECT statements.

Mastering Excel for Data Science

Excel is the most popular spreadsheet software data scientists use to analyze and visualize their data. In this course, you’ll learn how to use Excel for data analysis by exploring different functions that can be used on a spreadsheet. You’ll also learn how to create charts and graphs intuitively with simple steps.

Using this skill set, you’ll take advantage of all the benefits of using Excel, such as speed, ease of use, and flexibility in terms of formatting options.

You’ll also learn how to create a basic spreadsheet that contains all the data you need for analysis. Finally, you’ll learn how to use Excel’s built-in functions to analyze your data and create charts and graphs of your findings. By the end of this course, you will have learned everything there is to know about using Excel for data analysis.

Getting Started with Python for Data Science

Python is a programming language that has become more popular recently as the preferred choice for data analysis. It’s easy to learn and has a large community of developers who write tools for it.

Python is used for machine learning, natural language processing (NLP), and data visualization—a wide range of applications that can be applied to all business problems. The software team at LinkedIn uses Python extensively in their product development process; we also use it internally when building our internal systems.

Learning R for Data Science

R is a programming language that can be used to analyze and visualize data. The R Project is open source, meaning it’s free for anyone to use or modify.

There are many different packages available for analyzing and visualizing data in R:

  • ggplot2 (graphical plotting)
  • dplyr (data manipulation)
  • lattice (statistical analysis)

ggplot2 is a plotting system that can create beautiful, informative graphs. The package includes many plots, including scatterplots, histograms, boxplots, and tree maps.

You can also create complex plots with many layers by combining different data plots. dplyr is a fast and efficient data manipulation system that allows you to select, filter quickly, and arrange your data. It works with both local and remote files.

The package also has many functions that allow you to do the math on your data, such as summing or averaging values. Lattice is a statistical analysis system that enables you to do many types of calculations on your data, including linear modeling and regression analysis. It also has functions for working with categorical, ordinal, and continuous variables.

Learning to use Pivot Tables in Excel

Pivot tables are a powerful Excel feature that lets you summarize data in many different ways. They can create dashboards, reports, and charts quickly and easily.

Learning to use pivot tables to summarize data and create charts, graphs, and KPIs from your data table is helpful.

Financial Modelling in Excel

Financial modeling is the process of creating a model to analyze and evaluate the financial performance of a company, product, or service. This can be done using operational data to predict future costs and benefits or using historical data to project trends into the future.

The critical difference between forecasting and financial modeling is that forecasting uses historical information to predict events. In contrast, an economic model uses only recent data to predict future costs, revenues, and profits (or losses).

Deep Learning Fundamentals

Deep learning is a branch of machine learning that uses artificial neural networks to learn and process information. The network consists of multiple layers, each performing a specific task and then passing the results to the next layer.

In deep learning, we use this concept to create highly accurate models that can identify patterns in data without explicit instructions from human experts. In other words: artificial intelligence! 

In deep learning, we use this concept to create highly accurate models that can identify patterns in data without explicit instructions from human experts. In other words: artificial intelligence! 

Deep learning has become increasingly popular in recent years because it can be used for many tasks ranging from text analysis (e.g., spam detection) to speech recognition (the technology behind Siri).

Business Analysis Foundations for Success

Business analysis is a critical function in every business. The business analyst is responsible for understanding the problems and needs of the organization, as well as identifying data that can be used to solve them. They work with a wide range of stakeholders and have a wide range of skills, but they all share one thing—the need to understand how all pieces fit together so they can make informed decisions about what’s best for their organization. Need expert guidance? Connect with our data analysis specialists for tailored insights. Contact Us

Business analysts typically report directly to an executive team or high-level managers with authority over the entire department or division. They will generally spend time working on projects directly related to improving processes within areas with an identified need (for example, customer service). Still, they may also participate in projects focused on innovation (for example, new product development).

Use this Path to Get Started as a Data Analyst

This path is for anyone who wants to become a data analyst. You can use it to get started as a data analyst or continue your education in this field.

Before taking this path, you must know how to use SQL, Excel, Python, and R because they are all used by data analysts daily. These languages allow you to perform complex calculations quickly and efficiently while also being able to store large amounts of information so that it’s easy for others (like yourself) who aren’t familiar with the topic at hand can understand what’s going on visually or numerically without having any prior knowledge about what the problem was initially meant for them.

Conclusion

If you want to become a data analyst, you can learn by taking some online courses. Many types of data analyst training programs can help you achieve your goals and impact the world. These programs are designed for beginners and experts alike, so no matter what stage of your career or where you see yourself in the future, there’s something here for everyone! Why wait? [Start Now] by hiring us to bring your vision to life.

Since this learning path aims to help you become a data analyst, we’ve ensured that every step is relevant to the real world. You won’t find anything here about using Python or R for data science; these are tools for beginners who want to learn more advanced techniques like neural networks or machine learning. Instead, we’re focused on showing you how to use your skills in Excel, Python, and SQL so that when someone asks, “What does a data analyst do?” you can answer confidently!

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Sana Shafaqat

Sana Shafaqat

I am a professional statistician and data analyst. I have worked in the field for over five years and have experience with various statistical software packages. I am passionate about data analysis and interpretation and love finding new ways to visualize data. I enjoy reading, spending time with my family, and playing tennis in my spare time.

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