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Tag: become a successful data scientist

Top Courses to Help You Become A Successful Data Scientist

Posted on by Imarticus Learning

Data science can be an asset to any career by allowing professionals to make informed decisions based on data-driven insights. It can assist professionals in identifying trends, patterns, and opportunities in their respective industries, leading to improved business outcomes. 

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Data science skills are in high demand, and individuals with them are sought after in a variety of industries. Investing time in learning data science can thus be a wise career move that expands opportunities for professional development. Data science and analytics courses help learners do just that.

This blog lists the top 15 data science courses to look out for.

Top 15 Data Science Courses

  • Harvard University’s Data Science Professional Certificate

This course is intended for people with a programming and statistics background who want to improve their data science skills. Probability, statistics, data exploration, and data visualisation are among the topics covered.

  • IBM Data Science Professional Certificate

This programme covers all aspects of data science, including data analysis, data visualisation, and machine learning. It covers data science tools and techniques like Python, Jupyter Notebook, and SQL.

  • Google Data Analytics Professional Certificate

This is one of the best data analytics courses for beginners that introduce the fundamental concepts of data analytics, such as data cleaning, visualisation, and analysis. It teaches practical skills using Google Analytics and provides real-world scenarios to apply what is learned.

  • Johns Hopkins University’s Data Science Specialisation

This course covers important topics like data manipulation, exploratory data analysis, and statistical inference. It teaches how to clean, visualise, and analyse data using R programming.

  • Imarticus Learning’s Postgraduate Programme in Data Science and Analytics

This programme teaches data science fundamentals such as data analysis, machine learning, and deep learning. It also offers mentorship and career counselling. This is a great course for those wishing to pursue a career in data science.

  • Udacity’s Data Analyst Nanodegree

This course teaches students how to analyse and visualise data using Python, SQL, and statistics. It covers topics like data manipulation, data analysis, and data visualisation.

  • Coursera’s Applied Data Science Specialisation

This course teaches fundamental data science skills like data manipulation, data visualisation, and machine learning. It also addresses critical issues such as data ethics and privacy.

  • edX’s Data Science Essentials

This edX programme offers a thorough introduction to data science, covering topics such as data cleaning, data visualisation, and exploratory data analysis. Python programming and machine learning techniques are also covered.

  • MIT’s Statistics and Data Science MicroMasters Program

This programme includes advanced topics such as statistical inference, machine learning, and big data. It also covers data ethics and privacy and real-world case studies to apply the learned theoretical knowledge.

  • Dataquest’s Data Analyst Path

This course teaches students how to extract insights from data using Python, SQL, and data analysis techniques. It includes sections on data cleaning, data visualisation, and statistical analysis.

  • Springboard’s Data Science Career Track

This programme offers one-on-one mentoring and covers fundamental data science skills such as data analysis, machine learning, and data visualisation. It also offers real-world projects and the chance to collaborate with industry experts.

  • Udemy’s Data Science A-Z

Statistics, data visualisation, and machine learning are among the topics covered in this course. It teaches students how to use programming languages such as Python, R, and Tableau.

  • Microsoft Professional Program in Data Science

This programme teaches fundamental data science skills like data exploration, data analysis, and machine learning. It makes use of tools and technologies from Microsoft Azure.

  • DataCamp’s Data Scientist with Python Track

This course teaches students how to manipulate, visualise, and analyse data using Python. It covers data science libraries such as NumPy, Pandas, and Scikit-Learn.

  • Kaggle’s Machine Learning Courses

Kaggle provides several free courses that teach students to solve real-world problems using machine learning techniques. It covers fundamental concepts such as regression, classification, and clustering.

Why Data Science Courses?

Data science courses can help one’s career by providing in-demand skills and knowledge needed to thrive in today’s data-driven world. Professionals with data science skills can analyse and interpret complex data sets, identify patterns, and make sound decisions.

Learning data science can provide numerous benefits, such as enhancing your problem-solving skills, increasing your earning potential, and staying competitive in a rapidly evolving job market. With the growing demand for data-centric decision-making across industries, learning data science has become a valuable and in-demand skill set to help you achieve your career goals.

Conclusion

Data science is a rapidly expanding field with numerous job opportunities in various industries. Pursuing data science courses can improve one’s career prospects by providing new job opportunities, career advancement, and higher pay. Taking data science courses can be a wise investment in one’s professional development.

If you are aiming for a career in data science, you can check out the Postgraduate Programme in Data Science and Analytics by Imarticus. This programme can help you learn all the necessary skills and technologies essential to become a successful data scientist or data analyst. 

What Are The Steps To Become a Data Scientist From a Non-Technical Field?

Posted on by Imarticus Learning

A data scientist is a professional who is in the authority of collecting, analyzing and understanding large amounts of data. This job deals with advanced analytics technologies like machine learning and predictive modeling. Some of the basic responsibilities of this role include collecting and analyzing data, using different types of reporting tools to detect the trends, patterns, and relationships in various data sets. In today’s scenario, a data scientist is one of the best professions to pick as a career.

Scope of a non-technical person as a data scientist

To understand the scope, firstly, the term non-technical person should be defined. It refers to a person from a non-engineering background. Basically, the person may be from any educational background but should have the right approach. He or she should be ready to put in a lot of time and effort. Self-motivation is a must to mentally prepare oneself to learn whatever is essential to become a successful data scientist.

Try enrolling in a good data science course to give shape to the career. Eventually, you will realize the time invested in learning the matter will prove to be one of the best long term investments in your career.

Steps to become a data scientist from a non-technical field

Coming from a non-technical background, to become a successful data scientist following this six-step guide can prove to be really helpful:

  • Broaden the skill level with the help of a planned course – For those who are a fresher in the area of data science, they should enroll in a well-structured course. An ideal syllabus should cover the basics of the programming languages R and Java, Big Data handling, deep learning a part of machine learning, data visualization, probability, and statistics.
  • Get in touch with some mentors in this field – When venturing into any new field, a mentor’s guidance plays an important role in guiding one through the best path. Getting in touch with an experienced person helps in building a network and getting valuable lessons.
  • Try to attend every event held in the town or online – Attending such events are a great way to gather information from industry experts in this In-person events are better as that has a scope of open conversations.
  • Appear for mock interviews – Only preparations are not enough. When one is looking for a job opportunity in data science, having a basic idea of what the hiring managers are seeking, is very important. Attending mock interviews is the best way to measure one’s expertise level.
  • Never compromise with the basics – If one is serious about a career in data science, then familiarity with any one of the basic areas is really important. This helps in tuning the intellectual capacities of analyzing and interpreting data.
  • Stay open to learning new things – In the field of data science, remember there is no end to learning. So, don’t hesitate to learn new things from your peers or seniors as you move ahead in your career path. But remember there should be a methodical approach in whatever you do. Keep strengthening your basic knowledge and read books related to data science.

Conclusion

Following these simple steps will make one’s transition from a non-technical field to the domain of data science not only interesting but also hassle-free. To get the real feel of this process of shifting, it is recommended that one checks out the online videos of some real-life examples of people who made it possible and are successful data scientists today.