In recent years, Data Science and Machine Learning fields has been exploding with lots of companies looking for Machine Learning engineers to help them build great products.
It’s best to get started by learning about the field if you haven’t, data science has attractive wages but it isn’t for everyone. If DS looks like your jam then start off with some of the best data science books, Machine Learning: a Probabilistic Perspective by Kevin Patrick Murphy is my favorite but there are many more. Some form of training is almost essential, many people recommend starting with a data science bootcamp at the minimum, and a CS degree is preferred (though you can make it without one).
Data science is the application of science to the study of data. It’s either academic or (more popularly in recent years) used to help businesses derive useful information from raw data. Many companies (Google for example) gather petabytes of data that could be useful in determining (for example) what people are looking for in a product or service, or whether the results on a Google search page are fulfilling the searcher’s intent. Data scientists develop and use complex algorithms to turn this raw data into these useful insights, data products, or product recommendations. They employ machine learning, AI, and analytical tools along with solid interpersonal skills to do this.
I’ve seen most junior data scientists start at around $60-70,000 yearly, although that number changes depending on the cost of living in your area. Data scientists in places like LA and NYC get higher salaries, and a seasoned data scientist can make much more, easily breaking six figures.
While it is certainly common for data scientists to have either a bachelor's or master's degree (typically in computer science), a degree becoming less of a requirement for data science and data analytics positions. As the need for qualified and skilled data scientists has continued to increase in recent years, many businesses (large and small) have begun to lift their degree requirements for these lucrative and fulfilling jobs. For many companies, it's far more important for you to demonstrate your skills and experience, rather than for you to have a specific degree. Thus, data science bootcamps have become an increasingly viable option for aspiring data scientists and analysts. These short-term programs focus on teaching students the practical skills they need to succeed in data science positions and provide extensive opportunities to gain hands-on experience to help them demonstrate their skills to prospective employers.
The field of data science is a vast one, so it would be impossible to sum up all of the skills a particular data scientist might need. That said, here are some of the most common data science skills that could be required, depending on the particular data science field and the specific position: - Data analytics - Data processing - SQL (Structured Query Language) - Problem solving - Data visualization - Artificial intelligence - Apache Spark - Python libraries - R (programming language) - Big data - Data wrangling - Cloud computing Again, these are just a few of the common skills needed for many data scientist jobs. The actual skills required will depend greatly on your specific position and field.
While you can learn the basics of data science and data analytics fairly quickly, becoming a full-fledged data scientist takes some time. Data science is a science field, like marine biology or biophysics. Many data scientists hold master’s degrees, and while this isn’t a requirement, it gives an idea of how much time is really required to learn data science. That said, you can build a solid foundation for learning and mastering data science with a data science bootcamp. These are short-term training programs that provide practical data science skills on which you can build a career as a data scientist.
What does a Data Scientist do? They design data modeling processes, create algorithms and predictive models to extract the data the business needs, then help analyze the data and share insights with peers Specific tasks include: -Identifying the data-analytics problems that offer the greatest opportunities to the organization -Determining the correct data sets and variables -Collecting large sets of structured and unstructured data from disparate sources -Cleaning and validating the data to ensure accuracy, completeness, and uniformity -Devising and applying models and algorithms to mine the stores of big data -Analyzing the data to identify patterns and trends Interpreting the data to discover solutions and opportunities -Communicating findings to stakeholders using visualization and other means As I specified in a previous post: How does Data Science help a business? Having a Data Scientist among your employees will help greatly! Any business that receives electronic payment, is online based, or has social media presence will depend on a good scientist. They can analyze which age range within your clientele you can impact and how to do it. Which days of the week and hours have been more productive than others, or maybe if in the medical reference, number of patients may have been seen for flu and have tested positive for it vs. how many haven't tested positive. Tools used by Data Scientists: -Frameworks like Hadoop, Mahout, Apache, Hive and Pig -Programming languages, such as R, Java, Python, and SQL -Git/GitHub -Programming language interfaces like Jupyter Notebooks -Orange, IBM Watson and other automated machine learning architecture building frameworks -Data visualization tools like D3.js and Tableau -Databases like NoSQL, MongoDB, Cassandra, and MySQL -Programming language packages like Pandas, Numpy, Scipy, and Matplotlib