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These lessons are part of AryngFrom the 2018 Analytics Academy series for people interested in a career in analytics.

If you devour everything that is analytic, even to the point of configuring it

Google
alerts to help you get started or progress in your career in analytics, this series of blogs in five lessons will be useful.

These lessons are part of Aryng's series of analyzes for people looking to transition to a career in analysis or beginner analysis. I hope to answer all the questions that I have received from readers of my blog. Before going any further, understand your suitability for an analytical role by evaluating your own analytical skills.. If you do not have a great analytical capacity, you will not have fun as an analyst.

Lesson 1 – Understand the Analytical Landscape and Identify Your Ideal Analytical Job

So, what constitutes an analytical work? Is it the same as Big Data work?

The world of analysis is full of overused and overused terms. Therefore, before going any further, let me briefly clarify some terminologies. (This topic is discussed in depth in my book, "Behind Every Good Decision""so do not hesitate to start there too.)

Believe it or not, "analytics" is not synonymous with "Big Data," even though, nowadays, it is often mentioned in the same breath. Let's discuss it in a moment.

First of all, let's define "analytics" in relation to "business intelligence" (BI). The business intelligence and analysis are actually two separate processes that involve different tools and serve different purposes.

When a user interacts with a system (for example, when you buy groceries in your local supermarket), the data is produced, collected, cleaned, and stored using data solutions. , especially:

Teradata
, Hadoop and

Oracle
. Data is then accessed via reports and, increasingly, through graphical dashboards. BI includes all the components of the operation, from the moment the data is collected at the moment of their access..

The analysis, meanwhile, is the process performed on the data provided by BI in order to generate information to make decisions, actions and possibly impacts on income or otherwise. Data is converted into information using analysis tools such as

SAS
, R and Excel.

Now let's talk about Big Data. The ever increasing volumes, variety, and speed of Big Data (known as Three Vs) pose storage and visualization problems that make traditional business intelligence systems unstable. Big Data is therefore a business intelligence problem, not a problem of analysis. Our goal for this lesson must therefore exclude Big Data.

Which analytics jobs are you interested in?

Once you know that the analysis interests you, the question is: "What type of analysis work is right for you?", Give an idea of ​​the analysis tasks by typing " Analyst, "Analytics" or "Data Scientist" in the job. on forums such as LinkedIn, Icrunchdata.com or Monster. Below are some of the key titles you will find, mapped into three main categories. I will discuss the differences between these categories at the latest. Note: If the title includes "Analyst" But the job does not require data analysis, it is not a work of analysis. For example, a "Business Process Analyst" does not have any analysis work and we will not talk about these careers here.

In the table above, let's take, for example, Marketing Analyst. Most jobs with this title belong to the Business Analytics Professional job category. Some of these positions require advanced analytics skills and fall under the Predictive Analytics Professional category. Data Scientist, on the other hand, is used in a very broad and very vague way, jobs falling into three categories. Some job descriptions from Data Scientist seem to be looking for strong candidates in all three areas, which is not a very likely combination. I would recommend ignoring these jobs for now, as this could take a lifetime of learning to become this "superhuman" data scientist.

Let's talk about job classes – Data Analyst, Business Analytics Professional, and Predictive Analytics Professional. Everyone needs different analytical skills, according to the table below. For example, a business analysis professional needs strong business analytics skills, as well as the ability to access data through a graphical interface-based BI tool and the ability to access the data. analyze in a basic analysis tool such as MS Excel. An understanding of basic statistics and, perhaps, test skills may also be necessary. Note that, as with any job, these positions require additional skills specific to the industry served and the function.

So what jobs should you aim for? Most professionals with a background in BI / data or engineering, ie with experience in data structure, information management, data architecture, engineering, etc., can easily switch to a data analyst position. If you have a professional background – product managers, project managers, MBA – consider a business analysis work. And if your experience is in statistics, operational research, computer science or algorithms, a professional job in predictive analytics may be right for you.

When browsing the available jobs, browse the job requirements. What skills and tools are listed (expert knowledge of SQL, ability to make decisions based on analysis, etc.)? Use this information and the table above to identify the appropriate category of work. Now, based on your background, your own interests, and your experience in the industry, select the title of your dream analytics job in the categories that are right for you. For example, if you have 5 years experience as a data architect in the retail sector, your ideal job category in the area of ​​analysis would belong to the same sector as the analyst analyst. data.

Congratulations!

You are now about to find and achieve this dream job. My next blog will help you identify your gaps in analytical skills and your professional requirements in relation to your own context.

Meanwhile, if you're ready to begin your career transition in the field of analysis in 2018, sign up for my FREE 60-minute master class on 5 steps to successfully transition your career to the next level. Analytics and data science. We broadcast live in your time zone.

My book on Amazon | Follow @AnalyticsQueen | http://www.aryng.com | Contact Aryng | Call Piyanka at 408.412.7279

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These lessons are part of the Aryng Analytics Academy 2018 series for those interested in a career in analytics.

If you devour everything that is analytic, even to the point of configuring it
Google
alerts to help you get started or progress in your career in analytics, this series of blogs in five lessons will be useful.

These lessons are part of Aryng's series of analyzes for people looking to transition to a career in analysis or beginner analysis. I hope to answer all the questions that I have received from readers of my blog. Before going any further, understand your suitability for an analytical role by evaluating your own analytical skills.. If you do not have a great analytical capacity, you will not have fun as an analyst.

Lesson 1 – Understand the Analytical Landscape and Identify Your Ideal Analytical Job

So, what constitutes an analytical work? Is it the same as Big Data work?

The world of analysis is full of overused and overused terms. Therefore, before going any further, let me briefly clarify some terminologies. (This topic is discussed in depth in my book, "Behind Every Good Decision," so feel free to start there too.)

Believe it or not, "analytics" is not synonymous with "Big Data," even though, nowadays, it is often mentioned in the same breath. Let's discuss it in a moment.

First of all, let's define "analytics" in relation to "business intelligence" (BI). The business intelligence and analysis are actually two separate processes that involve different tools and serve different purposes.

When a user interacts with a system (for example, when you buy groceries in your local supermarket), the data is produced, collected, cleaned, and stored using data solutions. , especially:
Teradata
, Hadoop and
Oracle
. Data is then accessed via reports and, increasingly, through graphical dashboards. BI includes all the components of the operation, from the moment the data is collected at the moment of their access..

The analysis, meanwhile, is the process performed on the data provided by BI in order to generate information to make decisions, actions and possibly impacts on income or otherwise. Data is converted into information using analysis tools such as
SAS
, R and Excel.

Now let's talk about Big Data. The ever increasing volumes, variety, and speed of Big Data (known as Three Vs) pose storage and visualization problems that make traditional business intelligence systems unstable. Big Data is therefore a business intelligence problem, not a problem of analysis. Our goal for this lesson must therefore exclude Big Data.

Which analytics jobs are you interested in?

Once you know that the analysis interests you, the question is: "What type of analysis work is right for you?", Give an idea of ​​the analysis tasks by typing " Analyst, "Analytics" or "Data Scientist" in the job. on forums such as LinkedIn, Icrunchdata.com or Monster. Below are some of the key titles you will find, mapped into three main categories. I will discuss the differences between these categories at the latest. Note: If the title includes "Analyst" But the job does not require data analysis, it is not a work of analysis. For example, a "Business Process Analyst" does not have any analysis work and we will not talk about these careers here.

In the table above, let's take, for example, Marketing Analyst. Most jobs with this title belong to the Business Analytics Professional job category. Some of these positions require advanced analytics skills and fall under the Predictive Analytics Professional category. Data Scientist, on the other hand, is used in a very broad and very vague way, jobs falling into three categories. Some job descriptions from Data Scientist seem to be looking for strong candidates in all three areas, which is not a very likely combination. I would recommend ignoring these jobs for now, as this could take a lifetime of learning to become this "superhuman" data scientist.

Let's talk about job classes – Data Analyst, Business Analytics Professional, and Predictive Analytics Professional. Everyone needs different analytical skills, according to the table below. For example, a business analysis professional needs strong business analytics skills, as well as the ability to access data through a graphical interface-based BI tool and the ability to access the data. analyze in a basic analysis tool such as MS Excel. An understanding of basic statistics and, perhaps, test skills may also be necessary. Note that, as with any job, these positions require additional skills specific to the industry served and the function.

So what jobs should you aim for? Most professionals with a background in BI / data or engineering, ie with experience in data structure, information management, data architecture, engineering, etc., can easily switch to a data analyst position. If you have a professional background – product managers, project managers, MBA – consider a business analysis work. And if your experience is in statistics, operational research, computer science or algorithms, a professional job in predictive analytics may be right for you.

When browsing the available jobs, browse the job requirements. What skills and tools are listed (expert knowledge of SQL, ability to make decisions based on analysis, etc.)? Use this information and the table above to identify the appropriate category of work. Now, based on your background, your own interests, and your experience in the industry, select the title of your dream analytics job in the categories that are right for you. For example, if you have 5 years experience as a data architect in the retail sector, your ideal job category in the area of ​​analysis would belong to the same sector as the analyst analyst. data.

Congratulations!

You are now about to find and achieve this dream job. My next blog will help you identify your gaps in analytical skills and your professional requirements in relation to your own context.

Meanwhile, if you're ready to begin your career transition in the field of analysis in 2018, sign up for my FREE 60-minute master class on 5 steps to successfully transition your career to the next level. Analytics and data science. We broadcast live in your time zone.

My book on Amazon | Follow @AnalyticsQueen | http://www.aryng.com | Contact Aryng | Call Piyanka at 408.412.7279