In this video I compare the benefits of pursuing a data science certificate versus a bootcamp or masters degree. These all can be reasonable options depending on your personal situation. Watch this video to see which one may be right for you
Data Science Certificate
– Positives (Inexpensive, low time commitment, can help you build out your portfolio)
– Negatives (Likely wont get you a job, not very academically rigerous)
– Good for someone with a phd looking to get data science on their resume or a beginner who wants to see if data science would be interesting to them
Data Science Bootcamp
– Positives (Good networking and resume resources, reasonable academic rigor)
– Negatives (Very expensive, hit or miss with education, very time consuming)
– Good for someone who has a good portfolio and needs the networking help, someone with a phd in an unrelated field or masters in a related field, or someone who has extra cash to spend
Data Science Masters degree
– Positives (checks the graduate degree box, access to university network and resources, internship opportunities, decent exit opportunities)
– Negatives (Most expensive, very time consuming)
– This is a good option for someone who is already an analyst and wants to move into a data science role or someone who wants to become a data analyst but has no relevant experience.
5 Things to prepare before a Data Analyst interview:
In this video, we will go through the 5 things you need to prepare before a data analyst interview. Data analyst interview questions and answers & more.
1 – Research the Company & the Role
The first thing you should do, is simply go to the company’s website and read about the company. You should make notes about:
• What does the companies do?
• When it was founded and by whom?
• Who runs the company now; CEO name?
• What is the company’s mission & vision?
• What are the company’s core values?
• What industry is the company operating in? (tech? retail? Finance, etc)
• Who is the company trading with? Who are their clients / customers?
• Learn about what the analytics team you are about to join does
2 – Prepare for the most universal & frequently asked questions
There are some “universal” interview questions which apply to all roles (not just Data Analysts) and are almost always asked in interviews. Make sure you prepare for the following questions:
• Tell me about yourself?
o Talk about your academic background and focus on the data related modules / projects. Talk about your work experience with a focus on the analytical tasks you were performing. Close with a reason “why you are there”; which is obviously going to be that you love analytics and you love that company 😊.
• Why you want to join this company?
o Pick the “thing / project” that the company completed from their website and talk about how that “thing / project” excites you and makes you want to work for that firm
• Why you want to become a Data Analyst or pursue a career in data analytics?
o Pick a data related project or a data related task you were doing that you love doing it and talk about that
• What do you know about this company?
o Talk about what I mentioned in Step 1 (beginning of the article)
• Why should I hire you?
o Talk about all the data related skills you have and make sure you link them with what the Job Description asks the candidate to have. This “link” is crucial
• What are your strengths?
o Pick 2 / 3 data related strengths and talk about times you applied them
• What are your weaknesses?
o Pick a weakness which is not a major in data analytics like “I get nervous speaking in large groups” and make sure you talk about the steps you are taking / took to overcome this.
3 – Brush your technical skills
The majority of the data analyst interviews require you to take some sort of test; which is usually an Excel test. If you are applying to an experienced data analyst job then you will probably need to brush your SQL skills too. For the Excel test, make sure you practice the following:
• Creating / sorting / filtering tables
• Aggregating data
• Summarizing data
• Creating graphs / charts
• Pivot Tables
4 – Prepare to speak about a “Data” project you completed
Even if you are not asked, try to find a way to talk about a data project you have completed either at University, on-line or during work experience. This is an opportunity to show the interviewer that you have experience working on data projects. Talk about the “problem formulation” phase, the data gathering phase, the cleaning phase, the transformation phase, the tools used for the analysis, the techniques you used for the analysis and the outcome of the project.
5 Bonus tip – Link all your answers with data related skills / tasks
Since you cannot possibly prepare for all possible questions, especially the ones about your previous work experience, you will need to make sure that you link all your answers to data related skills. For example, if the interviewer asks you to tell him about your work experience, make sure you flash out skills like communication skills, management skills, speak about the analytical tasks you were performing daily and the related tools like Excel. Don’ just say “I used to work as a “barman” and my job was to serve drinks. Expand on data related skills. I have made a video where I speak about the data related skills (link in the description).
Looking for advice on how to become a Data Scientist? Zach Berman was a research specialist who saw his work increasingly taken over by automation, and knew he had to get ahead of the curve. After trying to learn solo, he looked for a more structured path, and found that Thinkful was the top of every list for bootcamps. He took Data Science Flex, scored a job as a data analyst at Home Depot, and found the things he studied at Thinkful put him ahead of analysts who had been there for many years. Now, he’s working for a big name company in a field that’s only continuing to grow.
These data science projects should be where you start and not featured on your resume. Data science projects that are on unique data sets and that are more advanced will help you get a job. These 3 projects will help you learn the concepts and get you familiar with python.
In this video, we will learn what are the skills required to choose Data Analyst as a Career option for one. Also, we will look at the scope of a person who looks forward to working as a Data Analyst.
We will also look at the tools required for while working as a Data Analyst. And what education and Degrees are required in order to be a Data Analyst. Also in this video, the importance of Data Analyst in the corporate world is Discussed.
All the doubts Related to Data Analyst and Data Science are Discussed in detail in this video.
1. Business Analyst
2. Management Reporting
3. Corporate Strategy Analyst
4. Budget Analyst
5. Social Media Data Analyst
6. Data Analyst
How to Become A Data Analyst | Data Analyst Career Path
#DataAnalyst #DataScience #Promo
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1) Student life in England
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3) Indian students
4) Informative videos for Students
5) Student living expenses
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And some other main things about this channel are Indian Vlogger in England,
Indian Youtube in England which shows various activities around the UK,
Student life in England,
How Students in England should choose the Course,
Indian Students in England,
Indian Students Life in England,
The main things covered in this video are:
1. Indian Vlogger in England
2. Indian Youtube in England which shows various activities around the UK.
3. Student life in England
4. How Students in England should choose the Course
5. Indian Students in England
6. Indian Students Life in England
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This Data Analytics video talks about the Data Analytics job growth and the top 6 Data Analytics job roles – Data Analyst, Business Analyst, Database Administrator, Data Engineer, Data Scientist, and Machine Learning Engineer. The growth of data has brought with it several job opportunities as organizations are always on the lookout for professionals who can draw meaningful insights from raw data and help businesses in crucial decision making. Hence, a career in this field is up-and-coming. This video will help you have an understanding of the various responsibilities, skills required, and the salary structure of each of the job role. You will also see the multiple companies hiring each of these professionals. Finally, we will show you a sample resume of a Data Analyst.
You will learn the below topics in this Data Analytics Jobs:
1. The Growth of Data – 0:30
2. Job role of a Data Analyst – 3:30
3. Job role of a Business Analyst – 6:43
4. Job role of a Database Administrator – 9:43
5. Job role of a Data Engineer – 12:21
6. Job role of a Data Scientist – 14:45
7. Job role of a Machine Learning Engineer – 17:56
8. Resume of a Data Analyst – 20:42
This Data Analyst Master’s Program in collaboration with IBM will make you an expert in data analytics. In this Data Analytics course, you’ll learn analytics tools and techniques, how to work with SQL databases, the languages of R and Python, how to create data visualizations, and how to apply statistics and predictive analytics in a business environment.
What are the learning objectives?
Simplilearn’s Data Analyst Master’s Program developed in collaboration with IBM will provide you with extensive expertise in the booming data analytics field. This data analytics certification training course will teach you how to master descriptive and inferential statistics, hypothesis testing, regression analysis, data blending, data extracts, and forecasting. Through this course, you will also gain expertise in data visualization techniques using Tableau and Power BI, learning how to organize data and design dashboards. In this data analyst certification online course, a special emphasis is placed on those currently employed in the non-technical workforce. Through this Data Analytics course, those with a basic understanding of mathematical concepts will be able to complete the course and become an expert in data analytics. This learning experience melds the knowledge of Data Analytics with hands-on demos and projects via CloudLab. Upon completing this course, you will have all the skills required to become a successful data analyst.
Why become Data Analyst?
By 2020, the World Economic Forum forecasts that data analysts will be in demand due to increasing data collection and usage. Organizations view data analysis as one of the most crucial future specialties due to the value that can be derived from data. Data is more abundant and accessible than ever in today’s business environment. In fact, 2.5 quintillion bytes of data are created each day.
The facts are that professionals who enter the Data Science field will have their pick of jobs and enjoy lucrative salaries. According to an IBM report, data and analytics jobs are predicted to increase by 15 percent to 2.72 million jobs by 2020, with the most significant demand for data analysts in finance, insurance, and information technology. Data analysts earn an average pay of $67,377 in 2019 according to Glassdoor.
Who should take up this course?
Aspiring professionals of any educational background with an analytical frame of mind are best suited to pursue the Data Analyst Master’s Program, including:
1. IT professionals
2. Banking and finance professionals
3. Marketing managers
4. Sales professionals
5. Supply chain network managers
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How to Transition Into Data Science: From Economics to Data Science? Econ majors often want to know how to transition into data science from economics. ✅Get 20% OFF the data science training! https://bit.ly/2x6YK95
In this video we discuss how to transition into data science. Today, we’ll be making the switch from economics and examine the good, the bad and the ugly. We’ll answer some of the most important questions running through your mind, like: “Can I”, “Should I” and “How can I” transition into data science from economics. And we’ll discuss the pros and cons before finding the best way to transition into Data Science.
Let’s start with “Can I make the transition into data science?”. The answer here is a resounding “yes!”. Roughly 13% of current data scientists have an Economics degree. For comparison, the most well-represented discipline is data science and analysis, which takes up 21% of the pie. Therefore, Economics is a competitive discipline when it comes to data science.
First, unlike STEM disciplines, social studies help develop great presentational skills which are essential for any data scientist. Through presentations and open discussions, students learn how to present a topic, as well as argue for or against a given statement. These activities result in developing a confident and credible way of showcasing actionable insights. Moreover, most econ majors deeply care about human behavior and response to different stimuli.
Furthermore, Economics frequently intertwines with Mathematics, Finance, Psychology, and Politics. Therefore, an economist’s approach is always meant to be interdisciplinary.
Finally, the technical capabilities of an economist are often quite impressive. An average economist has a good understanding of Machine Learning without really referring to it as such. Linear regressions and logistic regressions are studied in almost all Economics degrees.