How To Start Career In Data Science


Understand Which Of The Jobs You Like The Most

How to Start a Career in Data Science Course

The main goal of any data team is to create data-based solutions to help the business. This is a quite broad and maybe understated objective, simply because there are many forms to achieve it:

  • Developing models capable of predicting complex variables, such as product pricing or customer propensity.
  • Reducing the processing time for relevant information that might help the customer choose our product.
  • Finding patterns that might indicate market tendencies, or some necessity of the public.
  • And many other use cases that we can employ data-driven solutions to bring value to the business.

Usually, a data team has many roles: data scientist, data analyst, data engineer, and machine learning engineer are some of these roles.

This is the ecosystem of a data team, and one thing all these roles have in common is: they all need to understand really well their own activities, but also a little bit of the activities of the other roles. And all of them need to understand the business they are working on.

I need to emphasize that it is not as glamorous as it sounds. Most of the work is dealing with data in form of tables and graphs, and not using some kind of robot-making magic tool.

Now that youve chosen the role that you like the most, what you should do next?

How Difficult Is Data Science

No two data science jobs are the same. Each one calls for specific competencies and comes with its own set of responsibilities. So, how hard is data science? For some, studying data science may be challenging at first. Data science is a multidisciplinary field that combines technical know-how with soft skills . Those joining the workforce may find that there is a fairly high barrier to entry, too.

Across industries, the profession has unique complexities and comprises a great deal of problem-solving. And on top of that, the field is constantly evolving. But there are multiple opportunities to learn along the way, and with ample preparation, a career in data science is achievable. Data scientists help create practical solutions that advance an organizations goalsand they learn how to do that by obtaining a robust knowledge base and skill set. In addition, to a professional degree, data scientists can choose to pursue a variety of data science certifications and other professional development resources to grow their knowledge, while bolstering their credentials.

Being intellectually curious, paying close attention to detail, acting as a proactive problem solver and being open to learning new skills on the job are a few personal traits that may help these professionals to be effective in the workplace, master core competencies and keep up with new developments in the field over the course of their careers.

Resources For Data Science Majors

Because data science often relies on new and emerging technologies, students and professionals must stay up-to-date with the latest developments. Professional organizations disseminate news and updates in the field, and publications provide readers with in-depth analyses and research.

Students and professionals can also access open courseware to bolster their credentials and learn about new areas of data science.

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S To Become A Data Scientist

A data scientist finds trends and patterns in datasets, communicates recommendations to other teams, creates algorithms and data models to forecast outcomes and incorporates machine learning techniques to improve the quality of data. Below are a few steps that can help you build a career in data science.

Step 1: Earn a data science degree:

This is not always required but you can consider studying statistics and computer science in order to grasp the basic concepts.

Step 2: Hone the relevant skills:

It is a good idea to polish a few essential skills, such as programming , data visualisation and ability to work with tools like Tableau, PowerBI and Excel Big Data. This will, in turn, enable you to process Apache Spark and Hadoop.

Step 3: Gain experience:

Pick an entry-level data analytics job to gain experience and build a foundation for your data science career path. You can look for positions of a business intelligence analyst, data engineer, statistician, or data engineer.

Step 4: Prepare for interviews:

Consider preparing for interviews for a data scientists position. It will help you make a confident and knowledgeable impression when you apply for different types of data science jobs. A few questions commonly asked at interviews include the pros and cons of a linear model, using SQL to find data duplicates and the definition of random forest and machine learning.

Women In Data Science

Starting a Career in Data Science: The Ultimate Guide

According to a 2020 study by the Boston Consulting Group, only 15% of data scientists are women. That lack of diversity is a serious issue, the study says: “AI algorithms are susceptible to bias, so building them requires a team that includes a wide range of views and experiences.”

“Nobody has more power than the person with data. Even if you feel like you have imposter syndrome, well-analyzed data is your confidence booster.”

edX Head of Analytics L. Sam Bishop agrees, and urges women to join the field, despite some of the traditional tech perceptions and barriers that block or dissuade women and other underrepresented groups from pursuing data science and analytics careers. The most wonderful thing about data is that data is power, says Bishop. Nobody has more power than the person with the data. Even if you feel like you have imposter syndrome, well-analyzed data is your confidence-booster.

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Linear Algebra And Multivariable Calculus

One of the most basic data science skills is understanding advanced mathematics, usually taught after high school. Linear Algebra and Multivariable calculus are the backbones of most algorithms in data science. Thus, understanding them in depth will help you better understand which algorithms are likely to suit a given problem.

Does The Job Assistance Program Guarantee Me A Job

Apparently, no. Our job assistance program is aimed at helping you land in your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.

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Data And Analytics Manager

Data and analytics managers lead data science teams. It can be thought of as a luxury position, as not all businesses and organizations have a large enough data team to require a specific managerial position outside the confines of the other data science roles. For larger organizations that have a large team of data professionals, though, a data and analytics manager can be key to making sure everything runs smoothly.

These management positions not only possess data science technical skills, but also leadership and project management experience. They manage a variety of positions including but not limited to data engineers, data scientists, and data analysts and will often be promoted to their role. They also often serve as the spokesperson for the department, which can require the same level of polished verbal and communication skills that make for a successful data scientist. This role is a senior position that requires an advanced degree as well as many years of supervisory experience. In some businesses and organizations, the data team may even be run by a C-level executive role, often known as the chief data officer.

Is A Career In Data Science Worth It

How to Start A Career in Data Science: My Story

Yes, a job in data science offers excellent opportunities for the future. Data science professions are among the most rapidly increasing and in-demand in technology.

According to the US Bureau of Labor Statistics, demand for data science skills will rise by 27.9 percent by 2026.

According to a McKinsey report, this will result in a shortage of between 140,000 and 190,000 analysts in the United States alone.

In addition to the 1.5 million managers and analysts who will need to understand how data analysis drives decision making.

Glassdoor has referred to a Data Scientist job as the greatest job in America and Linkedin has labeled this job title as the most promising career because of its high demand, attractive salary, and all other benefits that come within.

Further resources:

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Data Science Career Faqs

  • What is a data scientist?

    A data scientist is a professional who has a multidisciplinary skill set and works with large amounts of data to find insights and answers to business problems. Data scientists typically have a postgraduate degree in a technical subject such as computer science or statistics.

  • Is data science a good career?

    Data science is an excellent career choice. According to the U.S. Bureau of Labor Statistics, data science is one of the fastest growing and highest-paid fields in the country.

  • What kinds of jobs can you get with data science?

    You can get a data science job in virtually any field. From retail to finance and banking, almost every industry needs the help of data science professionals to collect and process insights from their datasets.

  • Is it hard to get a data science job?

    Getting a data science job can be hard because the data science field is very new. Because of that, the field is constantly changing, so you need to stay on top of new skills and knowledge.

  • How do I start a career in data science with no experience?

    There are a few ways you can start a career in data science if you have no experience. One way is to incrementally build fundamental data science skills and knowledge such as applied statistics, data modeling, data management and warehousing, and deep learning. Explore edX courses and programs that can help you get started.

  • Learn Key Programming Languages For Data Science

    Data Scientists rely on a number of specialized tools and programs developed specifically for data cleaning, analysis, and modeling. In addition to general-purpose Excel, Data Scientists need to be familiar with a statistical programming language like Python, R, or Hive, and query languages like SQL.

    One of a Data Scientists most important tools is RStudio Server, which supports a development environment for working with R on a server. Open-source Jupyter Notebook is another popular application, comprising statistical modeling, data viz, machine learning functions, and more.

    Key Data Science Programming Languages and Tools

    Data science increasingly involves machine learning as well tools that apply artificial intelligence to give systems the ability to learn and become more accurate without being explicitly programmed.

    The tools used for machine learning depend to a large extent on the application that is, whether youre training the computer to identify images, for example, or extract trends from social media posts.

    Depending on their objectives, Data Scientists might choose from a wide range of tools including, TensorFlow, Apache Mahout, and Accord.Net.

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    Beginner Mistakes To Avoid When Starting Your Data Science Career

    If you wish to pursue a career in data science, you can save yourself days, weeks, or even months of frustration by avoiding these 9 costly beginner mistakes.

    If youre not careful, these mistakes will eat away at your most valuable resources: your time, energy, and motivation.

    Weve broken them into three categories:

    • Mistakes while learning data science
    • Mistakes when applying for a job
    • Mistakes during job interviews

    Is Data Science Hard

    Eight Step Plan for Starting a Data Science Career  Skill Monk

    Whether or not data science is hard really depends on your background and whether you enjoy working with numbers and data. While data scientists do not need as much software engineering or machine learning as data engineers, you will need to learn how to code in order to build predictive models.

    Data science has a steep learning curve, involving tough problems, a large amount of data, technical expertise, and domain knowledge, but luckily there are many free online resources to help you get started as an entry-level data scientist. Hopefully, you enjoy learning because data scientists are constantly upskilling and learning new technologies.

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    Which Data Science Industries You Should Consider

    Where do data scientists work? As it turns out, everywhere! The data boom creates demand for data scientist roles across an ever-growing variety of industries. From Finance and insurance, through Agriculture and Forestry, to Construction every organization needs solid data science skills to exploit their data and gain a competitive edge. So, what type of data scientist would you like to be? Here are 3 top industries consider:

    What Does A Data Scientist Do

    Data scientists spend some of their time analyzing datasets and looking for insights that can help businesses improve key metrics. But thats not the only thing that data scientists do.

    Data scientists work with key stakeholders at businesses to find out what their major challenges are. This lays the foundation for the data science process. The challenges of a business become problem statements for data scientists.

    They then proceed to build data models and write algorithms that can process data. All of this is done with the goal of finding solutions to the initial problem statement. Data scientists use various techniques to make this happen, including mathematical analysis, predictive modeling skills, natural language processing, regression analysis, deep learning, and analytical thinking.

    Data sciences applications are virtually unlimited. For example, in the marketing sector, digital marketing agencies are always on the lookout for ways to enhance the marketing ROI of their campaigns. There are armies of data scientists working on this problem to see how customers can get the most bang for their buck.

    Get To Know Other Data Science Students

    Mengqin Gong

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    What Kind Of Projects Are Included As Part Of The Training

    Intellipaat is offering you the most updated, relevant, and high-value real-world projects as part of the training program. This way, you can implement the learning that you have acquired in real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.

    You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.

    Take On Side Projects Or Internships

    How do I start my career in Data Science?

    To build your resume, companies will want to see professional practical experience. As you start building out your knowledge base, you can apply your skill-set in real-world settings and get real-time feedback.

    You can use freelancing platforms like Upwork or Fiverr, as well as search for part-time work or internships through social media and job boards. Kaggle also offers competitions with monetary prizes.

    Before interviewing, make sure to practice solving coding problems on LeetCode and explore potential data science interview questions.

    Show examples of past work samples on Github, LinkedIn or a personal website to build a good portfolio and a strong online presence.

    It can be hard to gain experience without experience, but by leveraging online communities and starting small, you can prove that you have what it takes to turn data science knowledge into measurable business outcomes.

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    How To Start Career In Data Science

    • Get Expert Advice On How to start a career in Data Science
    • Know the right path to become a Data Scientist
    • Know the right Skill to master to become a Data Scientist
    • Know the right industry for your background
    • Get to know which resources to use
    • Get to know what mistakes to avoid
    • Get to know how to crack your Data Science Interviews

    Data Scientist Career Outlook

    You can enjoy a bright career in data science, provided you have the right qualifications. The demand for individuals with these skills will continue to increase, and those already in data science roles are sure to see their salaries increase in the future. Indeed reports that Data Scientists in the United States can earn a yearly average of USD 113,251, and Payscale shows Data Scientists in India earning an annual average of 824562.

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    Build A Solid Project Portfolio

    It would help if you tailored your portfolio to fit the exact data science career that interests you. For instance, if you’re interested in machine learning, your portfolio should reflect projects involving machine learning. You can build your portfolio by working on personal projects, including building case studies that apply to specific problems affecting businesses.

    You can also apply for internships in companies willing to take data science interns for short-term projects. Furthermore, you can decide to work for free for friends that are business owners and non-governmental organizations. Some organizations you can apply for internships and jobs at are:

    Focus On Developing Necessary Soft And Business

    Starting a Career in Data Science: The Ultimate Guide

    To succeed as a data scientist, you must possess soft and hard skills. Hence, it would be best if you had a solid grasp of the principles of statistical and programming abilities and predictive modeling, data visualization, data manipulation, and data analysis skills.

    Furthermore, to get an entry-level position in data science, you should demonstrate the following capabilities:

    • Work well in a team,
    • Manage your time effectively,
    • Proactive problem-solving skills,

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    Overestimating The Value Of Academic Degrees

    Sometimes, graduates can overestimate the value of their education. While a strong degree in a related field can definitely boost your chances, its neither sufficient nor is it usually the most important factor.

    To be clear, were not saying graduates are arrogant

    In most cases, whats taught in an academic setting is simply too different from the machine learning applied in businesses. Working with deadlines, clients, and technical roadblocks necessitate practical tradeoffs that are not as urgent in academia.

    To avoid this mistake:

    • Supplement coursework with plenty of projects using real-world datasets.
    • Take relevant internships, even if they are part-time.
    • Reach out to local data scientists on LinkedIn for coffee chats.

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