An appealing career can be found in the capacity of data science. There’s a lot of demand for data talent, and no indication it will stop in the future. Gaining data skills increases your value to any employer in any industry.
We’re collecting a vast amount of data obtained, and that data is allowing us to make brand-new developments possible. We often don’t use the systems we have to create all the data. So our scientific discoveries are often changed.
Those in the field of big data are among the driving forces behind the next scientific revolution. Regardless of which path you choose—the sensible one, the risky one, or something somewhere in between—there are lots of exciting opportunities for work involving data.
How You Get Started
Udacity is committed to working with some of the most innovative companies in the industry. They work with companies that can help their students reach their career goals. Udacity helps some of the top employers and prospective candidates to work together and enables them to secure a lasting partnership that endures for years to come.
Udacity has brought together all these resources to offer you this guide on data, and all their specialists offer unique viewpoints and personal experiences. If you’re interested in working in data science, this article will give you a complete guide.
Career Paths for Data Science and Recommended Courses
Each time you post an article, message, or video to Facebook, click a link or buy anything online, you’re generating data. With more than 3.5 billion internet users and 2 billion mobile phone users, that’s a lot of data.
As data has increased, so has its ability to be collected, organized, and analyzed. Digital data storage is cheap compared to before. Processing power is more powerful than ever before.
And tools and software are available to analyze the large amount of information that is available for business intelligence. In the last few years, data analysis has carried out everything from predicting stock prices.
The very first step on your path to a professional in the field of data? Assessing your primary career options: Data Analyst, Data Scientist, and Data Engineer.
Data Analyst
Data analysts should have a background in at least five core competencies: programming, statistics, machine learning, data munging, and data visualization. Beyond their own technical expertise, data analysts must have a strong understanding of these core skills in addition to presentation abilities.
Required Skills
Data analysts should have a general knowledge of five fundamental skills: programming, statistics, machine learning, data munging, and data visualization. In addition to technical knowledge, attentiveness and interactive skill are important to be successful as a data analyst.
Recommended Courses
Step 1. Become a Data Analyst
Level: Intermediate
Estimated Time: 4 Months (10 hours per week)
Prerequisites: Python & SQL
What you will learn:
- Introduction to Data Analysis
- Practical Statistics
- Data Wrangling
- Data Visualization with Python
Step 2. Programming for Data Science with Python
Level: Beginner
Estimated Time: 3 Months (10 hours per week)
Prerequisites: None
What you will learn:
- Introduction to SQL
- Introduction to Python Programming
- Introduction to Version Control
Data Scientist
Some companies treat the titles “Data Scientist” and “Data Analyst” as interchangeable terms, but there’s an essential distinction between them. The data science expert is good at solving complex data-related problems.
The data analyst knows how to analyze different kinds of information and communicate with it effectively. Data Scientists are in high demand, but there is currently a shortage of them in the workforce.
Acquire programming skills, data wrangling, machine learning, experiment design, and data visualization skills, and launch a career in data science.
Required Skills
Compared to a data analyst, a data scientist looks at data from multiple resources. Data Scientists use tools such as Hadoop (a commonly used framework for data processing), they use programming languages such as Python and R, and apply the practices of advanced mathematics, statistics, and information theory.
Recommended Courses
Step 1. Become a Data Scientist
Level: Advanced
Estimated Time: 4 Months (10 hours per week)
Prerequisites: Python & SQL & Statistics
What you will learn:
- Solving Data Science Problems
- Software Engineering for Data Scientists
- Data Engineering for Data Scientists
- Experiment Design and Recommendations
- Data Science Projects
Step 2. Become a Data Analyst
Level: Intermediate
Estimated Time: 4 Months (10 hours per week)
Prerequisites: Python & SQL
What you will learn:
- Introduction to Data Analysis
- Practical Statistics
- Data Wrangling
- Data Visualization with Python
Step 3. Programming for Data Science with Python
Level: Beginner
Estimated Time: 3 Months (10 hours per week)
Prerequisites: None
What you will learn:
- Introduction to SQL
- Introduction to Python Programming
- Introduction to Version Control
Data Engineer
A data engineer builds a robust, fault-tolerant data pipeline that cleans, transforms, and aggregates unorganized and messy data into databases or data sources. Data engineers are typically software engineers.
Data engineers are responsible for compiling and maintaining database systems, managing logic queries, scaling to numerous platforms simultaneously, and maintaining disaster recovery systems.
Required Skills
Data engineers make certain that data can flow smoothly from source to destination so it can be processed. As such, data engineers specialize in the understanding of and expertise in:
- Hadoop-based technologies: MapReduce, Hive, and Pig
- SQL-based technologies: PostgreSQL and MySQL
- NoSQL technologies: Cassandra and MongoDB
- Data warehousing solutions
Recommended Courses
Step 1. Data Streaming
Level: Advanced
Estimated Time: 2 Months (5-10 hours per week)
Prerequisites: Intermediate Python, SQL, and experience with ETL
What you will learn:
- Foundations of Data Streaming
- Streaming API Development and Documentation
Step 2. Programming for Data Science with Python
Level: Beginner
Estimated Time: 3 Months (10 hours per week)
Prerequisites: None
What you will learn:
- Introduction to SQL
- Introduction to Python Programming
- Introduction to Version Control
Step 3. Become a Data Engineer
Level: Intermediate
Estimated Time: 5 Months (5-10 hours per week)
Prerequisites: Intermediate Python & SQL
What you will learn:
- Data Modeling
- Cloud Data Warehouses
- Spark and Data Lakes
- Data Pipelines with Airflow
- Capstone Project
The Starting Point
Data analysis is a swiftly developing field with many voices sharing what you will need to know and in what order. The available information may be challenging, overpowering, and discouraging.
Keep in mind that the information we just shared is a guide to what you need to know to land your first data analyst job. You can use this information as a starting point, depending on your specific background.
No matter how much experience you have, the initial cost to enter the data analysis field is high. You need to be focused and determined if you want to make it in this field. Nevertheless, your reward for your efforts is bigger.
Tomorrow of Data
According to SAS, the definition of Predictive Analytics is “the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to provide the best assessment of what will happen in the future.”
In a recent TDWI study, the top five factors driving companies toward Predictive Analytics: predict trends, understanding customers, improving business performance, driving strategic decision-making, and predicting behavior.
More companies are using predictive analytics in their data processing strategies. Markets and Markets reported that “the global predictive analytics market size to grow from UDD 10.5 billion in 2021 to 28.1 billion by 2026.”
As Predictive Analytics has gained popularity, its benefits are becoming ever more important. The ability to build such forward-looking analysis is quickly becoming an important part of any business’ competitive edge. However, effectively conducting data analysis now requires two things: data management tools, and people who can use them.
Conclusion
As we have mentioned in the introduction to this guide, a career in data is both secure and safe. We’ve also provided an overview of why pursuing a career in an employment area is an excellent choice for so many people.
No matter what you choose to do, there are some basic skills and abilities you will need. However, data provides a range of opportunities with different entry points.
Here is a suggested article for you to read: What is Data Science?
