Commonly referred to as the “oil of the 21st century," our digital data carries the most importance in the field. It has incalculable benefits in business, research and our everyday lives. Your route to work, your most recent Google search for the nearest coffee shop, your Instagram post about what you ate, and even the health data from your fitness tracker are all important to different data scientists in different ways. Sifting through massive lakes of data, looking for connections and patterns, data science is responsible for bringing us new products, delivering breakthrough insights and making our lives more convenient.
Glassdoor’s recently released report which highlights the 50 best jobs in recent times. Unsurprisingly, Data Scientist Jobs is at the top spot for the second year in a row with a score of 4.8/ 5. Data science is one of the fastest-growing careers in many countries, and projected to grow by more than 30% over the next decade. Data science has also topped LinkedIn’s Emerging Jobs Report for three years
Data science involves a plethora of disciplines and expertise areas to produce a holistic, thorough and refined look into raw data. Data scientists must be skilled in everything from data engineering, math, statistics, advanced computing and visualizations to be able to effectively sift through muddled masses of information and communicate only the most vital bits that will help drive innovation and efficiency.
Data scientists also rely heavily on artificial intelligence, especially its subfields of machine learning and deep learning, to create models and make predictions using algorithms and other techniques.
WHAT CAN DATA SCIENCE BE USED FOR?
- Anomaly detection (fraud, disease, crime, etc.)
- Automation and decision-making (background checks, credit worthiness, etc.)
- Classifications (in an email server, this could mean classifying emails as “important” or “junk”)
- Forecasting (sales, revenue and customer retention)
- Pattern detection (weather patterns, financial market patterns, etc.)
- Recognition (facial, voice, text, etc.)
- Recommendations (based on learned preferences, recommendation engines can refer you to movies, restaurants and books you may like.
- Self-driving cars
- Choice forecasting
Data Analytics vs. Data Science
While data analysts and data scientists both work with data, the main difference lies in what they do with it. Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. They analyze well-defined sets of data using an arsenal of different tools to answer tangible business needs.
Data scientists, on the other hand, design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.
Data scientists are typically tasked with designing data modeling processes, as well as creating algorithms and predictive models to extract the information needed by an organization to solve complex problems.
Marketing analytics
Marketing analytics is the study of data garnered through marketing campaigns in order to discern patterns between such things as how a campaign contributed to conversions, consumer behavior, regional preferences, creative preferences and much more. Marketing analytics benefits both marketers and consumers.
In-Demand Data Science Careers
· Data Scientist
· Machine Learning Engineer
· Machine Learning Scientist
· Applications Architect
· Enterprise Architect
· Data Architect
· Infrastructure Architect
· Data Engineer
· Data Analyst
· Statistician
Good Data Science courses
