According to the U.S. Bureau of Labor Statistics, data scientist jobs will grow 35 percent from 2022 to 2032. Around 17,700 job vacancies are projected to open for data scientists each year, over the forecasted period. Statistics is an important discipline in the field of data science and machine learning. Understanding the importance of statistics and their applications in these fields are significant skills in data science career.
What are Statistics?
Statistics is a mathematical branch that helps with data collection, analysis, interpretation, and presentation. However, some people consider it a collection of methods and tools for assessing, understanding, showing, and making decisions based on data.
Statistics comprise inferential and descriptive methods for a detailed understanding of data and allow confident decision-making. Statistics apply multiple statistical methods or algorithms on a data set to understand the values that can solve real problems.
Significance of Statistics in Data Science
A data scientist should learn statistics to push their data science career ahead. The reason is statistics connect data to the questions businesses encounter across various disciplines. For example, ways to boost revenue, control investment, improve interactions, and more.
To resolve real problems, statistics use multiple methods like mean, frequency analysis, median, mode, regression, variance analysis, etc. It performs analysis using standard techniques like mathematical formulas.
To become a successful data scientist, command over statistics must be powerful. Statistical analysis gives important findings explored by a dataset and summarizes crucial information. It calculates the data measurement via mathematical methods and does future estimations based on formerly recorded data. Moreover, testing experimental predictions becomes easier with statistical analysis.
Data scientists can use Power Query M, Excel, SAS, and other systems to clean and organize huge data sets. Knowledge of statistical functions allows data scientists to work within time constraints and budget. One must understand the main statistical terminologies to use statistics in data science. It includes population, sample, variable, and statistical parameters.
Role of Statistics in Machine Learning (ML)
Several ML performance measurements like precision, root mean squared error, recall, etc. are based on statistics. Data exploration is essential for data analysis. Data analysts use statistical methods to explain dataset size, accuracy, and quantity to comprehend the data nature. Data visualization and exploration help in finding unique and unexpected insights from data.
Combining these insights and statistics helps in encouraging discoveries in several AI (artificial intelligence) branches. Visualization tools make data more understandable. Statistical tools help in the prior identification of patterns and easily understand them. This eventually makes getting conclusions and formulating action plans easier.
How to Learn Statistics?
Learning more about statistics in a data science career and machine learning doesn’t need enrolling in regular classes at any university or institute. The United States Data Science Institute (USDSI®) provides an engaging and flexible way to study data science, machine learning, statistics, and more concepts without disrupting your regular lifestyle.
Certification courses are available for beginners, experienced professionals, and business leaders to develop and expand skills and outperform their competition. USDSI® certifies candidates who are interested in several upcoming data science majors. The institute is committed to certifying approximately 100K professionals by 2025 to overcome the shortage of clued-up data scientists.
The following are globally recognized and self-paced USDSI certification programs with a very flexible approach to learning from anywhere at any time and from any device. All certifications span 4-25 weeks with 8-10 hours per week hours of learning and provide a certificate and digital badge after successful completion.
1. Certified Data Science Professional (CDSP™)
This program is available for students and working experts with limited skills in data science and limited work experience. The candidate could adopt this program to learn about data science, mathematics and statistics for data science, performing data analysis, big data and Hadoop, data visualization, and more topics.
2. Certified Lead Data Scientist (CLDS™)
This course is available for professionals with at least 2 years of work experience to help them reduce the complexities of data science projects. It is designed with a focus on advanced data science fundamentals and other topics like Machine Learning and PowerBI, data analysis lifecycle, advanced big data analytics, NLP, and more.
3. Certified Senior Data Scientist (CSDS™)
Earning this certification is helpful for data scientists with at least five years of experience to be involved in decision-making at the organizational level. Those who are committed to developing advanced data scientist skills can apply for this certification. The CSDS™ certification helps candidates to become certified Senior Data Scientists by learning essential operations on data, DevOps and cloud computing, elastic stack, advanced dashboards with Grafana, and more topics.
Learning in detail about statistics is an important skill set for data scientists and machine learning experts. It helps in drawing meaningful conclusions through raw data analysis and covering all important concepts that are primarily used to make valuable sense of data and reach accurate conclusions.
In machine learning, understanding statistics helps make one aware of the effectiveness of models based on the evaluation. Thus, if you want to optimize your organizational operations, boost customer satisfaction, and maximize revenue, convert raw data into actionable insights by mastering statistics.