How Smart Machines Think By Sean Gerrish
Have you ever wondered about how self-driving cars work, or how your streaming service manages to find exactly what you want to watch, without you having to search for it? Wonder no more! This book, written by an expert machine learning engineer, outlines some of the key ideas that enable some of our smart machines to perceive and interact with the world, through the theory and practice of creating machine learning algorithms. For any data analyst looking to get into machine learning and artificial intelligence, this is a must-read.
Bonus reading: 12 Must-Read Data Analytics Blogs
Dabble With Machine Learning Algorithms
Using existing tools is one thing. However, data scientists often have to create solutions from scratch. Machine learning algorithms are a common example, and are often used in data science. Dabble with algorithms like or random forest to get a feel for how they work. Read around the topic and youll learn which ML algorithms work best for different data types, and which tasks they can be used to solve.
I Switched Careers At 34 And Became A Data Analyst Heres How
This article is written by The Woke Salaryman, and is sponsored by SkillsFuture Singapore, the government agency leading the SkillsFuture movement. This writer took data courses funded by SkillsFuture before committing to a more expensive course down the line.
About a year ago, I packed up my belongings, returned the company key card, and said my farewells to colleagues. I had spent seven years as a journalist. Two in corporate communications. My next destination was something quite different a data science bootcamp.
I had resignedfrom jobs before, but this was the first time I was quitting without another position already lined up.
Many would question the need for such a radical change. After all, why leave a stable career to pursue something that I had absolutely no experience in?
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Diagnostic Analytics: Why Did It Happen
The point of difference between descriptive and diagnostic analyses is that while descriptive analysis seeks to give an objective overview of whats happened, diagnostic analysis seeks to establish why those things may have happened. This can be done by identifying and handling outliers or anomalies within your data.
Visualize And Share Your Findings
The data has been analyzed and insights have been gathered. However, this isnt the end of the data analytics process: the data analyst must now present their findings in a way thats clear and easily understood by key stakeholders. In order to do this, an analyst may use visualization softwaresuch as Tableau or Microsoft Power BIthat will generate reports, dashboards, or interactive visualizations. At this stage of the process, its important that the data analyst is as clear and transparent in their findings as possible so that the relevant stakeholders can make informed decisions. You can learn more about data visualization here.
This is just a basic overview of the data analytics process. To learn more, read more in this article: A Step-by-Step Guide to the Data Analysis Process
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Lacking General Confidence In Your Abilities
Making major career changes is intimidating, especially when youre older. Even the most confident individuals can have occasional crises of self-esteem. Moving into a workplace full of young people can also encourage a sense of impostor syndrome. And concerns about ageism in the workplaceeven if you dont face any direct discriminationtakes a toll.
How to overcome this challenge: Celebrate every achievement, no matter how small. Start by celebrating your decision to change careers, which takes guts! More practically, it helps to write down any concerns you have, such as gaps in your knowledge, such as data analytics software you are unfamiliar with, or simply more abstract concerns. Compare this list against solid evidence of your abilities. Its a very effective way of identifying gaps in your skillset and showing where your strengths lie. Where there are gaps, fill them. Where there are strengths, play to them.
Data Analysts In Top Demand
According to the World Economic Forum, the top skills in a post-industrial economy include data analytics, artificial intelligence, Big Data, and machine learning. The breadth and depth of these disciplines now reach into nearly every aspect of society. Ninety-seven million new roles may emerge that are more adapted to the new division of labor between humans, machines, and algorithms, writes WEF author Kate Whiting.
Of the top three 21st-century jobs listed in WEFs research, data analysts and data scientists ranked number one, AI and machine learning specialists number two, and Big Data specialists at number three.
In an economy that demands career flexibility and a skills market needing data analysis expertise, the next trend supporting a career change to data analytics is how those skills are acquired.
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Why Do You Need To Perform Profile Building Activities Like Blogging Speaking At Meetups And Participating In Data Science Competitions
Let us say that you are interested in cricket, you learn and practice cricket daily but how will you grow yourself? It wont happen by practicing in nets daily! You must be recognized and get noticed for your talent by participating in a competition and getting in touch with potential trainers. Similarly, you must be recognized by potential recruiters and enthusiasts to grow yourself. Let us see how:
What Is A Data Analyst
A data analyst
- collects data from various sources
- organizes it effective to identify underlying relationships
- transforms it into a form for easy analysis
- performs statistical analysis,
- and visualizes it using various graphs, charts, and other visualizations to derive meaningful insights helpful for making profitable business decisions.
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How Much Experience With Solving Real
How many data science projects have you completed so far? This is a very common question interviewers ask in data science interviews. We have conducted hundreds of these interviews for both data analyst and data scientist roles and this is quite often the jackpot question.
This is especially true if youre a fresher or a relative newcomer to data science.
Data science projects offer you a promising way to kick-start your career in this field. Not only do you get to learn data science by applying it but you also get projects to showcase on your CV! Nowadays, recruiters evaluate a candidates potential by his/her work and dont put a lot of emphasis on certifications.
Is Data Science A Good Career For You
Our digital footprint has increased over the years and especially since the pandemic began. You may not realize and still be surrounded by massive amounts of data. The huge volume of data makes Data Science a coveted career in todays world. A 2020 Dice report said that the demand for data scientists increased by an average of 50% across healthcare, telecommunications, media, and the banking, financial services, and insurance sectors, among others. The ashes of this pandemic crisis have strengthened the data science job market making it the second-best job in America for 2021.
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How To Overcome Age
As weve established, being an older data analyst doesnt have to get in the way of a flourishing career. The main thing is to be honest about your strengths and weaknesses and to grab the opportunities available to you. And take the following steps to ensure that your age, far from harming your data analytics career, can work in your favor.
Proven Steps For Career Transition From Data Analyst To Data Scientist
In this article
How to make a career transition from Data Analyst to Data Scientist ? This is one of the most common questions our admission counselors come across. Our career counselors come across several talented data analysts who are keen to make a career transition to become a data scientist but are not sure where and how to begin.
I am a data analyst but keen on further advancing my career as a data scientist. I am bored of interrogating data to produce reports and recommendations, it does not seem exciting to me anymore. I am inclined to know more about a data scientist career path where I can use my skills to build robust machine learning models to solve business problems at scale. I am very excited about becoming a data scientist. How can I make the career transition from Data Analyst to Data Scientist?.
Are you thinking the same? Have you reached a crossroad in your professional life where you feel the need to upgrade your analytical skills or take up an exciting job role like that of a data scientist? Everybody has such moments in life. While a few get the right guidance and opportunity to accomplish it, others fail miserably. If you are one of those aspiring professionals looking to make a career transition from data analyst to a data scientist, this blog will be the best companion for you along your transition career path.
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Moving To Data Analytics From A Different Career
Can you switch things up professionally and move from accounting or risk management to data analytics? The first question you should be asking is, do you really want to? Have you done the research to fully understand what it will be like to work in data analytics? Or are you just interested in the trendy sounding AI and machine learning job titles?
Why Data Analytics?
Most data analysts must put in some hard graft before theyre handling the cutting edge technology youre fantasising about. So think very carefully, and take the time to consider what part of a data analysts role interests you, rather than the job title itself. This will help guide you in the right direction as you reroute your career path.
Understand what a data analyst does: they use technical tools to parse through large quantities of raw information in order to generate meaningful insights. Essentially, anything data-related will fall under their remit, whether its removing corrupted data or preparing reports for the business.
Good on Paper
Data analysts require strong critical thinking and communication skills. So, at the very least if you can demonstrate the ability to think analytically about data, look beyond the numbers to the patterns beyond, and clearly and effectively convey your findings, youll be in with a good chance.
The Portfolio
Get Technical
In the Know
Looking Ahead
Transitioning To Data Analytics
Career transitions dont happen overnight, and there is no shortcut to becoming a data analyst. While these steps may seem daunting, you might start to realize that youre having fun solving data problems, learning new tools, and crowdsourcing from others. When this happens, youll know that youve chosen the right path.
One of the best ways to prepare yourself to break into the field of analytics is to further your education, whether through online courses, bootcamps, or an advanced degree.
Northeastern University offers many degrees and graduate certificates focused on teaching students the knowledge and skills they need to be successful in an analytics-focused career.
For example, the Master of Professional Studies in Analytics is designed to prepare students for the in-demand and highly competitive field of data analytics. The program helps students build portfolios of real-world projects demonstrating competency with key technologies, visualization and communication techniques, and the ability to translate information into recommended actions.
If youre interested in building a career in analytics, take the first step by downloading our free, comprehensive guide below.
This post was originally written in March 2017. It has since been updated for accuracy and relevance.
About Kelsey Miller
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Do What You Can To Get Noticed At Work
Why not volunteer to run a lunch and learn training session at your office? Or even organize a company hackathon? The business you work for might not currently employ many data scientists but theres nothing like showing a bit of initiative to demonstrate your value. Make a good impression at work and you never know when it might come back aroundeven if its just in the form of a glowing recommendation to a future employer.
How To Switch Your Career To Data Science
Undeniably, Data science has become one of the hottest industries over the past few years from now. Being dominant in almost every sector, data science is powering up businesses and helping them in making business decisions and thats what makes it special and demand is rising like a storm in the market for such professionals. In fact, people with no such background have also taken their way toward data science and by going through different processes many have made a career transition.
Data Science is the study of data using tools and technologies to build predictive models and derive meaningful information. Career transition helps you in getting a Handsome Salary and alongside expanding your knowledge in various sectors. This is something called A Good Call. Now, the question arises, if youre already working in some domain then How to switch your career in Data Science?and to make your way smooth and provide you in-depth details, we have drafted this article that will guide you through all the way so that you can start your new path towards data science.
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Get Your First Data Analytics Job In 4 Steps
Making a career change can be scary, especially if self-doubt of Im not good enough starts creeping in. However, there is no point in staying in a job or career that no longer brings you joy or fulfills you professionally.
If youre reconsidering your career, youre not alone over the last two years, over 50% of employed Americans have considered a total career revamp. Chances are, you know a relative or friend who is going through a similar career dilemma right now.
If youre considering making a bold move to data analytics, weve got you covered. Understand if a career in Data Analytics is right for you in four easy steps.
Why Work In Analytics
According to projections from IBM and Burning Glass Insights, its estimated there will be more than 2.7 million job openings for professionals with data skills by 2020, up from 2.35 million in 2015.
And because nearly 40 percent of these jobs will require a masters degree or higher, its likely companies will not be able to fill open positions as readily as they would like. The same study shows that data science and analyst jobs already remain open for five days longer than average.
For those interested in transitioning into a career in analytics, this is good news. Qualified professionals looking to break into the analytics field can leverage their skills in order to earn handsome salaries. According to Robert Halfs 2019 Technology Salary Guide, individuals pursuing an analytics career can earn anywhere from $77,000 to $219,500 annually, depending on the job title and other factors.
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Why Get Certified As A Data Scientist
A question that we often hear from clients and colleagues is, “Why should I get a Data Science certification?” That is a fair question for most other areas of study and business. In areas such as finance or engineering, there are far more important accreditations you could and should achieve before hanging your shingle or trying to retool your skill set or career.
Data science is a broad discipline with a few accredited certification programs. However, many of those programs are cost-prohibitive.
There are at least 50 Data Science certification programs by universities worldwide offering degrees and diplomas in this discipline, writes data science blogger, Zeeshan Usman. It costs from $50,000 to $270,000 and takes one to four years of your life.
And although somewhat new in the nomenclature, data science encompasses many skills that professionals may already have acquired through work or educational experience such as:
A Fresh Perspective
Furthermore, Data Science certifications allow students to learn and hone skills that wont normally be acquired through work experiences, such as exploratory data analysis skills, data visualization skills, and data mining/machine learning algorithms.
Practice Presenting Your Findings
It can be easy to focus only on the technical aspects of data analysis, but donât neglect your communication skills. A significant element of working as a data analyst is presenting your findings to decision makers and other stakeholders in the company. When youâre able to tell a story with the data, you can help your organization make data-driven decisions.
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