Artificial intelligence or AI has penetrated into various fields and is one of the key technologies at the beginning of this century. However, to use these revolutionary tools, you must hire highly qualified experts. Machine learning engineer or ML engineer is one of them.
<h2>What is a Machine Learning Engineer?</h2>
The job of a machine learning engineer is similar to that of a data scientist. Both can handle large amounts of data, have strong data management skills, and can perform complex modeling on dynamic data sets.
However, the similarities end here. When data scientists generate reports and graphs to show the results of their data analysis, machine learning engineers design and create software that will automatically execute predictive models.
Every time the software performs an operation, it uses the result to perform future operations with increasing precision. Some of the most famous examples are the recommendation algorithms of Netflix, Amazon, TikTok or Spotify. Whenever a user watches a video, searches for a product, or listens to music, the companies mentioned before extract and store data. Machine learning recommendations have become more and more precise without the need for human intervention. The job of the machine learning engineer is to develop such algorithms. In short, the job of a machine learning engineer is a job that requires data science and software engineering skills. In large companies, ML engineers free data scientists from engineering tasks, allowing them to focus on mathematical modeling for instance.
<h2>What is a machine learning project?</h2>
To fully understand the role of a machine learning engineer, it is important to understand what a machine learning project is. It uses software engineering principles combined with data analysis and data science methods to create machine learning models.
For example, ML engineers working for TikTok are responsible for developing the platform's recommendation algorithm. He or she will then have to develop data pipelines and integrate them into TikTok app so that end users can benefit watch relevant videos.
<h2>What do machine learning engineers do every day?</h2>
On a daily basis, machine learning engineers will work with data scientists, software engineers, data engineers and UI/UX experts. They also work on internal machine learning platforms and develop new machine learning models.
Usually the data is unstructured, so machine learning must be used. Depending on the nature of the data, machine learrning can be applied in different ways. Machine learning algorithms allow, for example, to recognize images and speech. This will automatically analyze the content of the image and assign a label to it. Similarly, speech-to-text can be used to convert audio speech to text. Therefore, unstructured data can be converted into usable information. Machine learning also allows connections and associations to be established between data. This is very useful in deciphering consumer expectations. For example, a machine learning model can predict that customers who bought product A will like product X. This allows e-commerce companies such as Amazon to recommend products to their customers based on their previous purchases.
Furthermore, machine learning is also used in the financial field to predict risks and prevent fraud through real-time detection. A large amount of historical data can be analyzed to make predictions and determine investment potential or expected payment defaults.

What is a Machine Learning Engineer?

In this post, we have listed the 5 Best Masters in Business Analytics. 

Our goal?&nbsp;
Help you decide where you will study Data science and Analytics, because earning a degree in a prestigious university can be critical to land your first data scientist or machine learning job. 
 
This ranking is based on QS World University Rankings which has recently unveiled the <a href="https://www.topuniversities.com/university-rankings/business-masters-rankings/business-analytics/2020" target="_blank">Best Masters in Business Analytics for 2020</a>. According to <a href="https://www.techopedia.com/definition/344/business-analytics-ba" target="_blank">Techopedia</a>, Business analytics (BA) refers to all the methods and techniques that are used by an organization to measure performance. Business analytics are made up of statistical methods that can be applied to a specific project, process or product. 
 
TLDR: below is the list of the 5 best masters selected by QS World University Rankings. 
 

1.&nbsp;<a href="https://mitsloan.mit.edu/master-of-business-analytics" target="_blank">MIT (Sloan) Master of Business Analytics, Cambridge (MA), United States</a>
 
2.&nbsp;<a href="https://www.anderson.ucla.edu/degrees/master-of-science-in-business-analytics" target="_blank">UCLA (Anderson) Master of Science in Business Analytics, Los Angeles (CA), United States</a>
 
3.<a href="https://www.essec.edu/en/program/mscs/master-data-sciences-business-analytics/" target="_blank"> ESSEC/CentraleSupelec, Master in Data Sciences &amp; Business Analytics, Paris (France) and Singapore</a> 

4.<a href="https://www.mccombs.utexas.edu/Master-of-Science-in-Business-Analytics" target="_blank"> Texas (McCombs) Master of Science in Business Analytics, Austin (TX), United States</a> 

5.<a href="https://www.imperial.ac.uk/business-school/programmes/msc-business-analytics/" target="_blank"> Imperial College Business School, MSc Business Analytics, London (United Kingdom)</a>
<h1>&nbsp;1.&nbsp;MIT (Sloan) Master of Business Analytics</h1>
 
The MIT Sloan Master of Business Analytics’ objective is to train its students to become data science experts in order to solve business challenges. This program is divided in two parts with courses during Fall (Analytics and Machine Learning) and independent activities from January. If enrolled students want to work in the US, 100% of graduates of previous classes seeking a US role managed to&nbsp;<a href="https://www.datayoshi.com/" target="_blank">find a job</a> after completing this master.
The expected salary after finishing the Master of Business Analytics at MIT is around&nbsp;$110,000.
Tuition &amp; Fees:&nbsp; $82,368
Number of students admitted:&nbsp;60 →&nbsp;<a href="https://mitsloan.mit.edu/master-of-business-analytics/admissions/class-2020-profile" target="_blank">class profile&nbsp;</a>
Find more details on their&nbsp;<a href="https://mitsloan.mit.edu/master-of-business-analytics">website</a> 

<h1>2.&nbsp;UCLA (Anderson) Master of Science in Business Analytics 
</h1>
The UCLA Anderson Master of Science in Business Analytics is a 15-month on-campus program. This program teaches students analytics concepts in different fields such as Fraud, Business, Operations, etc. Students are required to find a <a href="https://www.datayoshi.com/">summer internship</a> to use the new skills they have acquired during their first 9 months at UCLA. Finally, they will be asked to work on a Capstone Analytics Project before graduating.&nbsp;
The expected salary after finishing the Master of Business Analytics at UCLA is&nbsp;$100,000 (the placement rate is 100%).
Tuition &amp; Fees:&nbsp; $66,709
Number of students admitted: 60 → <a href="https://www.anderson.ucla.edu/degrees/master-of-science-in-business-analytics/admissions/class-profile" target="_blank">class profile&nbsp;</a>
Find more details on their <a href="https://www.anderson.ucla.edu/degrees/master-of-science-in-business-analytics">website</a>
<h1>3.&nbsp;ESSEC/CentraleSupelec, Master in Data Sciences &amp; Business Analytics</h1>
 
The ESSEC/CentralSupelec offers a Master in both Data Science and Business Analytics that requires to study during one year. The program is divided in three parts (part 1:&nbsp;Refreshers &amp; Core courses, part 2: Major in Data Science, Business Analytics or Digital Strategy, part 3: Internship/Professional Experience).&nbsp;Students majoring in either&nbsp;Data Science&nbsp;or&nbsp;Business Analytics&nbsp;will acquire new skills such as Natural Language Processing, Deep Learning, Decision Analysis, etc.&nbsp;
 
The average salary after finishing this Master is&nbsp;$69,000.
 
Tuition &amp; Fees:&nbsp; €23,000
Find more details on their <a href="https://www.essec.edu/en/program/mscs/master-data-sciences-business-analytics/" target="_blank">website</a> 

<h1>4. Texas (McCombs) Master of Science in Business Analytics 
</h1>
The UCLA Anderson Master of Science in Business Analytics is a 10-month program that is willing to teach students how to harness vast amount of data and use it to help their future company make data-driven decisions (with courses such as Predictive Modeling, Data Analytics Programming, Marketing Analytics, etc).&nbsp; 
 
 
Tuition &amp; Fees: $43,000 (Texas resident) - $48,000 (Non-Texas Resident and International)
Number of students admitted:&nbsp;53 (52% female, 48% male)
Find more details on their <a href="https://www.mccombs.utexas.edu/Master-of-Science-in-Business-Analytics" target="_blank">website</a> 

<h1>5. Imperial College Business School, MSc Business Analytics</h1>
The MSc Business Analytics at Imperial College Business School is a one-year program that prepares graduates for a future of data-driven and evidence-based decision making. Students can either study on campus or online, the program is divided in three parts with core modules, five electives to choose from and projects to complete. After completing this MSc, 97% of the students <a href="https://www.datayoshi.com/">find a job in Analytics</a> within three months (with 46% in the UK) mostly in the Tech and Consultancy sectors.&nbsp;
Tuition &amp; Fees:&nbsp; £29,500
Number of students admitted:&nbsp;82 (on campus) →&nbsp;<a href="https://www.imperial.ac.uk/business-school/programmes/msc-business-analytics/meet-our-students/class-profile/" target="_blank">class profile&nbsp;</a>
Find more details on their&nbsp;<a href="https://www.imperial.ac.uk/business-school/programmes/msc-business-analytics/">website</a>

5 Best Masters In Business Analytics

If you’re thinking of becoming a data scientist or a data analyst, here is an article that will help you decide which job you might be a fit for. In this article, we will talk about responsibilities, qualifications, skills and salary. 

<h1>Data Scientist</h1>
<h2>Responsibilities</h2>
As a data scientist, you will have the following responsibilities:
<ul>
 <li>Identify opportunities by leveraging company data to drive business solutions</li>
 <li>Collect and determine the correct data sets that could stem from structured and unstructured data from various sources</li>
 <li>Ensure accuracy and completeness of data by cleaning and validating it</li>
 <li>Apply and develop models and algorithms using internal data sets</li>
 <li>Develop A/B testing framework</li>
 <li>Mine and conduct analysis of data to optimize and improve product development, marketing techniques and business strategies</li>
</ul>
<h2>Skills</h2>
As a data scientist, you will need to have the following skills to be successful:
<ul>
 <li>Statistical analysis and data mining skills (regression, statistical tests, random forest, text mining, properties of distributions, etc.) are useful to identify patterns in your company data or detect anomalies</li>
 <li>Knowing machine learning is a plus, your future employer will ask you to implement algorithms and statistical models using this technology</li>
 <li>Being able to write code with different languages such as Java, R, Python or SQL</li>
 <li>Having data storytelling skills to communicate and explain your actionable insights for a non-technical audience</li>
 <li>Excellent communication skills (both written and verbal) to be able to work with different stakeholders (engineering, product, business, leadership, etc.)</li>
</ul>
<h2>Qualifications</h2>
Usually data scientists have the following qualifications:
<ul>
 <li>Master Degree or higher is preferred</li>
 <li>PhD required for some positions</li>
 <li>Degree in Computer Science, Statistics, Mathematics, Economics or Physics</li>
</ul>
<h2>Salary</h2>
The salary for a data scientist depends usually on your location, but usually the average salary is quite high. Below, you will find the salary you can expect.
<ul>
 <li>San Francisco: <a href="https://www.payscale.com/research/US/Job=Data_Scientist/Salary/b497355d/San-Francisco-CA">$122,380</a></li>
 <li>Seattle: <a href="https://www.payscale.com/research/US/Job=Data_Scientist/Salary/d3a47291/Seattle-WA">$109,259</a>&nbsp;</li>
 <li>New York: <a href="https://www.payscale.com/research/US/Job=Data_Scientist/Salary/416ffb10/New-York-NY">$102,595</a></li>
 <li>Boston: <a href="https://www.payscale.com/research/US/Job=Data_Scientist/Salary/012464fa/Boston-MA">$101,301</a></li>
 <li>Los Angeles: <a href="https://www.payscale.com/research/US/Job=Data_Scientist/Salary/30ad1e0e/Los-Angeles-CA">$99,094</a></li>
 <li>Austin: <a href="https://www.payscale.com/research/US/Job=Data_Scientist/Salary/d70fff37/Austin-TX">$97,651</a></li>
</ul>
<h1>Data Analyst</h1>
<h2>Responsibilities</h2>
As a data analyst, you will have the following responsibilities:
<ul>
 <li>Work with business leaders to identify opportunities</li>
 <li>Create reports and dashboards internally and sometimes for external clients</li>
 <li>Manipulate, analyze and interpret complex data sets</li>
 <li>Acquire data from primary and secondary data sources</li>
 <li>Sift through business numbers and data to build a story from a hidden information</li>
</ul>
<h2>Skills</h2>
As a data analyst, you will need to have the following skills to be successful:
<ul>
 <li>Having coding skills in languages such as SQL, Python and/or R</li>
 <li>Being experienced with tools such as Microsoft Excel or Google Sheets</li>
 <li>Having analytical and problem-solving skills&nbsp;</li>
 <li>Being able to build reports and data visualizations using tools such as Tableau, Power BI or Looker</li>
 <li>Understanding of data warehousing and ETL techniques</li>
 <li>Knowledge of data cleaning and transforming techniques</li>
 <li>Having excellent written/verbal communication skills</li>
</ul>
<h2>Qualifications</h2>
Usually data analysts have the following qualifications:
<ul>
 <li>Bachelor Degree or higher is preferred</li>
 <li>Master required for some positions</li>
 <li>Degree in Computer Science, Statistics, Mathematics, Economics or Physics</li>
</ul>
<h2>Salary</h2>
The salary for a data analyst depends also usually on your location, but usually the average salary is a bit lower than the one for a data scientist. Below, you will find the salary you can expect.
<ul>
 <li>San Francisco: <a href="https://www.payscale.com/research/US/Job=Data_Analyst/Salary/2768b0e4/San-Francisco-CA">$80,905</a></li>
 <li>Seattle: <a href="https://www.payscale.com/research/US/Job=Data_Analyst/Salary/8636a81c/Seattle-WA">$65,988</a>&nbsp;</li>
 <li>New York: <a href="https://www.payscale.com/research/US/Job=Data_Analyst/Salary/bd48f6a2/New-York-NY">$66,396</a></li>
 <li>Boston: <a href="https://www.payscale.com/research/US/Job=Data_Analyst/Salary/e86205dd/Boston-MA">$65,104</a></li>
 <li>Los Angeles: <a href="https://www.payscale.com/research/US/Job=Data_Analyst/Salary/5e663f05/Los-Angeles-CA">$63,877</a></li>
 <li>Austin: <a href="https://www.payscale.com/research/US/Job=Data_Analyst/Salary/0820f5bc/Austin-TX">$60,557</a></li>
</ul>
Looking for a job? <a href="https://www.datayoshi.com/" target="_blank">Check our latest openings.</a>

Data Scientist versus Data Analyst

SQL (Structured Query Language) and Pandas (Python library built on top Numpy package) are widely used by data scientists, because these programming languages enable them to read, manipulate, write and retrieve data (most of the time stored in a database or data warehouses such as BigQuery or Amazon RedShift). Every data scientist is aware that her/his job will include data extraction and cleaning, therefore knowing SQL or Pandas is often required.
 
In this article, we will give you a simple comparison of those important tools for data scientists with a focus on the JOIN function (used to combine from two or more tables or dataframes).
​​
Let's go over the different JOINs available in SQL: 

<ol>
 <li>INNER JOIN: Returns records that have matching values in both tables 
 
</li>
 <li>LEFT JOIN:&nbsp;​Returns all records from the left table, and the matched records from the right table 
 
</li>
 <li>RIGHT JOIN:&nbsp;Returns all records from the right table, and the matched records from the left table 
 
</li>
 <li>FULL JOIN:&nbsp;Returns all records when there is a match in either left or right table</li>
</ol>
​
Let's dive in and now learn how to join two tables or data frames using SQL and Pandas 

1. INNER JOIN
 
SQL 

<pre><code>SELECT * 
FROM table1 
INNER JOIN table2 
&nbsp;&nbsp;&nbsp;&nbsp;ON table1.key = table2.key; 
</code></pre>
​
Pandas​
<pre><code>import pandas as pd 
 
#df1 and df2 are two pandas dataframes 
 
pd.merge(df1, df2, on='key') 
 
</code></pre>
​2. LEFT JOIN​
 
SQL 

<pre><code>SELECT * 
FROM table1 
LEFT JOIN table2 
&nbsp;&nbsp;&nbsp;&nbsp;ON table1.key = table2.key; 
</code></pre>
​
Pandas​
 
<pre><code>import pandas as pd 
 
#df1 and df2 are two pandas dataframes 
 
pd.merge(df1, df2, on='key',how='left') 
 
</code></pre>
3. RIGHT JOIN​
 
SQL 

<pre><code>SELECT * 
FROM table1 
RIGHT JOIN table2 
&nbsp;&nbsp;&nbsp;&nbsp;ON table1.key = table2.key; 
</code></pre>
​
Pandas​
 
<pre><code>import pandas as pd 
 
#df1 and df2 are two pandas dataframes 
 
pd.merge(df1, df2, on='key',how='right') 
 
</code></pre>
4. FULL JOIN​
 
SQL 

<pre><code>SELECT * 
FROM table1 
FULL JOIN table2 
&nbsp;&nbsp;&nbsp;&nbsp;ON table1.key = table2.key; 
</code></pre>
 
​Pandas​
 
<pre><code>import pandas as pd 
 
#df1 and df2 are two pandas dataframes 
 
pd.merge(df1, df2, on='key',how='outer') 
 
</code></pre>
​
We hope that you will use those snippets to join some tables or data frames in your daily job!

Pandas versus SQL Comparison : JOIN

AutoML is a buzz word that you hear more often, it can enable custom machine learning models at scale and generate an infinite amount of predictions without having to hiring a lot of data scientists.
 
<h2>What is AutoML?</h2>
 
According to Wikipedia: "Automated machine learning (AutoML) is the process of automating the process of applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model. AutoML was proposed as an artificial intelligence-based solution to the ever-growing challenge of applying machine learning.The high degree of automation in AutoML allows non-experts to make use of machine learning models and techniques without requiring becoming an expert in the field first."
AutoML is a disruptive machine learning method, because it can automate the entire processing chain of classic machine learning methods. The main objective is to reduce the need for data scientists to implement machine learning models.
When using AutoML, you only need to provide data to the machine, and it will automatically tell you what you want to know. With AutoML, you can fully automate predictive analysis, find relevant variables in the data, and choose the best algorithm. No data science skills are needed, and you don't have to know how it works. You only need to define your problem.
 
<h2>How companies can use AutoML?</h2>
 
AutoML could be helpful for companies since data scientists are hard to find in the job market. In 2019, the job search engine Indeed.com reported that the number of job postings for data scientists has tripled since end of 2013. Therefore, using AutoML can make small companies using machine learning techniques to improve their product or increase their revenue without having to compete with much more attractive tech companies for Data science talents. Companies such as Google or Facebook use AutoML to deploy unique machine learning models without having to hire data scientists to build models for each of their billions of users.&nbsp;
 
AutoML can be used for a variety of purposes, such as fraud Detection, pricing, sales management and marketing management. For companies who already have data scientists, it means that they can focus, for instance, on feature augmentation instead of spending too much time on the model creation.
 
<h2>Is it the death of data scientists?</h2>
 
However, AutoML cannot solve every problem, data scientists will still be crucial for companies that want to improve their business using data. AutoML is very useful, if you want to predict an outcome but if your goal is to help your sales team understand what products are more likely to be sold, you will still need data scientists with a high level of expertise.&nbsp;
According to McKinsey, data scientists will definitely be needed by companies in the long-term but probably less than that was anticipated a few years ago. Also, it is worth noting that AutoML doesn't have any business intuition, so it won't help you select the right training dataset before building the model and avoid creating biased models.

Our Blog

What is a Machine Learning Engineer?

5 Best Masters In Business Analytics

Data Scientist versus Data Analyst

Pandas versus SQL Comparison : JOIN

What is AutoML?