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Tag: data science career

A Step-By-Step Guide For A Smooth Career Transition To Data Science!

Posted on by Imarticus Learning
Reading Time: 2 minutes

Are you an electronics engineer looking to move to a data science career for a better-paying job? Or are you a commerce student who has recently taken to data science? This simple data science career guide will help you transition to the field and become a data scientist.

Step-By-Step Guide to Becoming a Data Scientist

Follow these four steps to move from any field to a data science career.

Step 1 – What field are you in right now?

The first and the most important step in this process is to understand your current academic and professional standing.

Are you still a student? Or are you working at a job in another field right now?

Step 2 – Gain Data Science Qualifications – Study or Work

If you are still studying, you have two options. Either wait and complete your existing degree (especially if only a few months are left) or immediately switch to a data science course. The latter can be a difficult situation, but when you look at the number of years you will further waste in that degree, it will make sense to you.

As a student, your goal should be to gain at least a degree, a certificate course, or a diploma in the field. Without an educational qualification, becoming a data scientist will be challenging. Then you will need a lot of hands-on experience and projects in your CV to prove that you are an able data scientist.

If you are working, the transition will take a bit longer for you. As noted, gaining some education in the field should be your higher priority. In case it is not possible due to financial or any other reasons, you can look at online courses.

Whatever you do, make sure you have some experience – academic or professional – before you move to step 3. In an ideal scenario, a little bit of both will improve your chances considerably.

Step 3 – Create a Solid Data Science CV

Making a data science career for yourself should start with you making a CV of yourself. Once you are satisfied with your own academic and professional qualifications, a CV is your pathway to reach out to potential employers.

Here is a list of activities that you need to do one after other:

  • Create a detailed CV that is no longer than two pages
  • Update and build your LinkedIn and Naukri.com profiles (among other job websites)
  • Create a web portfolio that lists our your academic and/or professional projects in the data science field

Once you have any or all of these three, move to the next step.

Step 4 – Apply for a Dozen Jobs Daily

The fastest way to build a data science career is to aggressively apply for jobs. You need to send out at least a dozen applications – via LinkedIn, Naukri.com, company career pages – every day to even get a response.

The Covid-19 situation can be a challenge here but staying aggressive is the only solution. Since a data science career entails working on a computer, employers are still looking for talent.

Following these four critical steps can pave your way to the goal of becoming a data scientist. It is not as easy as it looks and will require a lot of hard work on your part. Nor is it a quick way to get into a data science career.

On average, you can expect to spend at least 2 to 4 years in this process if you are looking for a successful transition regardless of your current professional situation.

What Are The Steps To Become a Data Scientist From a Non-Technical Field?

Posted on by Imarticus Learning
Reading Time: 2 minutes

A data scientist is a professional who is in the authority of collecting, analyzing and understanding large amounts of data. This job deals with advanced analytics technologies like machine learning and predictive modeling. Some of the basic responsibilities of this role include collecting and analyzing data, using different types of reporting tools to detect the trends, patterns, and relationships in various data sets. In today’s scenario, a data scientist is one of the best professions to pick as a career.

Scope of a non-technical person as a data scientist

To understand the scope, firstly, the term non-technical person should be defined. It refers to a person from a non-engineering background. Basically, the person may be from any educational background but should have the right approach. He or she should be ready to put in a lot of time and effort. Self-motivation is a must to mentally prepare oneself to learn whatever is essential to become a successful data scientist.

Try enrolling in a good data science course to give shape to the career. Eventually, you will realize the time invested in learning the matter will prove to be one of the best long term investments in your career.

Steps to become a data scientist from a non-technical field

Coming from a non-technical background, to become a successful data scientist following this six-step guide can prove to be really helpful:

  • Broaden the skill level with the help of a planned course – For those who are a fresher in the area of data science, they should enroll in a well-structured course. An ideal syllabus should cover the basics of the programming languages R and Java, Big Data handling, deep learning a part of machine learning, data visualization, probability, and statistics.
  • Get in touch with some mentors in this field – When venturing into any new field, a mentor’s guidance plays an important role in guiding one through the best path. Getting in touch with an experienced person helps in building a network and getting valuable lessons.
  • Try to attend every event held in the town or online – Attending such events are a great way to gather information from industry experts in this In-person events are better as that has a scope of open conversations.
  • Appear for mock interviews – Only preparations are not enough. When one is looking for a job opportunity in data science, having a basic idea of what the hiring managers are seeking, is very important. Attending mock interviews is the best way to measure one’s expertise level.
  • Never compromise with the basics – If one is serious about a career in data science, then familiarity with any one of the basic areas is really important. This helps in tuning the intellectual capacities of analyzing and interpreting data.
  • Stay open to learning new things – In the field of data science, remember there is no end to learning. So, don’t hesitate to learn new things from your peers or seniors as you move ahead in your career path. But remember there should be a methodical approach in whatever you do. Keep strengthening your basic knowledge and read books related to data science.

Conclusion

Following these simple steps will make one’s transition from a non-technical field to the domain of data science not only interesting but also hassle-free. To get the real feel of this process of shifting, it is recommended that one checks out the online videos of some real-life examples of people who made it possible and are successful data scientists today.

What Is A Data Scientist’s Career Path?

Posted on by Imarticus Learning
Reading Time: 2 minutes

The Data Career trajectory is probably the hottest career option you can do right now. As Glassdoor’s latest report shows, the $ 108,000 base salary is not only attractive to job seekers, but the Data Science career also boasts 4.2 out of 5.

Data Science Pipeline

A data science project is a whole process. It is important to understand this fact to get out of the labyrinth of data science.

Data science is not magic!

Embarking on a series of steps systematically first, the project goals are reached. Have you identified attractive business issues or market opportunities? You need to clarify what your company is trying to help you gain a competitive edge.

Next, you need to know where to collect data, plan resources, and coordinate people to do their job. The third part is data preparation. You must clear the data and investigate it carefully. The association begins to appear and the sample and the variable are corrected. The next step is to create, validate, evaluate and improve the form.

Finally, you need to communicate your team experience in the data science process. The data must be compelling and compelling. In the final reporting stage, visualization is essential to telling the complete story.

What did you learn?

At Imarticus Learning, the role of the data science team is not exclusive technology. Programming and statistics are essential to the basic steps in the Data Science Training, but contextual skills are essential to the planning and reporting stages. 

A role in data science

In fact, the role of data scientists is a common part of many different fields. Data scientists are highly capable professionals who have a big picture and are a data programmer, statistician, and a good storyteller.

However, the data science team counts people with different roles, all of whom contribute in different ways. If your career path in the data world is your ultimate goal, there are many ways to reach it.

For example, as an analyst, your data science career will be involved in day-to-day tasks that focus on data collection, database structure, modeling and execution, trend analysis, recommendations, and storytelling. Business intelligence (BI) analysts, on the other hand, should be able to see the trend and get an overview and state of the business unit in the market.

BI analysts usually have experience in business, management, economics or similar fields. However, you should also “interact with data”. BI analysts process a great deal of information and spend most of their time analyzing and illustrating data collected from multiple sources.

Are you fascinated by marketing issues? Marketing analysts are a special kind of data analyst. However, their main competency is associated with analyzing customer activities data with the help of special programs and not involved in programming or machine learning.

Data Science at Work

Data science training equips you with the skills for suggesting smart solutions for performing machine learning for beer and food molecules. Preparing beer with the right molecules to match the most popular meal ingredients on the market will be fun and make money. Imagine the perfect mix of top-selling beers like burgers and tikka masala!

How To Use Data Science For Predictive Maintenance?

Posted on by Imarticus Learning
Reading Time: 3 minutes

How To Use Data Science For Predictive Maintenance?

Most businesses constantly face an issue while analyzing whether their critical manufacturing systems are performing to their full capacity while ensuring a consistent reduction in the cost of maintenance. Causes of potential concerns need to be identified early to help organizations come up with more cost-effective plans.

This is where predictive analysis fits the bill. Predictive analysis is used to predict if an in-house machine will malfunction or work correctly. Predictive analysis also helps to plan maintenance in advance, predict failures, classify failure types and recommend necessary actions to be taken after a system fails. The scope of data science is vast and predictive analysis only helps in proving that further.

Factors influencing the success of predictive maintenance

There are three factors that influence if a predictive model is going to be successful or not:

Having the right data

One of the most crucial factors influencing predictive maintenance is having enough data that helps analyze factors that may lead to failure. Additional system features like operating conditions, technical properties also need to be taken into consideration. Additionally, it is also important to make an inventory that will help note the kinds of failure that can occur, and which are the ones that can be predicted.

If at all there is a failure, what the failure process might look like. Having the right data for predictive maintenance also helps understand which parts of the system may have failed and how improvement in terms of performance can be brought about. A system has a vast life span of over a couple of years, which means data collection needs to be done over a couple of years to ensure correct statistics are taken into consideration. A basic data science course will teach you everything about data collection methods.

Framing a predictive maintenance model

The next step is to decide the best modeling strategy for the collected data and how it can lead to the desired output. While there are always multiple modeling strategies to choose from, a predictive maintenance framing strategy should keep a couple of things in mind:

Desired output for the model

Quantity of data collected

Measurements required to predict is a system will succeed or fail

Advance time to predict before a system fails

Setting performance targets for the model such as accuracy, precision and more

Evaluating predictions in predictive maintenance model

A predictive maintenance model predicts whether a system will succeed or fail, what are the conditions under which it might fail and how to ensure that it runs smoothly amongst others. After a predictive model is built, it gets highly essential to analyze how accurate the predictions have been, under what circumstances has the model been able to predict a certain failure or success conditions, and what can be done to combat the same.

Usage of data science in predictive maintenance

Using data science in building predictive maintenance models goes a long way and has its own set of advantages. Here is a lowdown of the ways in which data science has proved to be beneficial for the same:

Minimizing the cost of maintenance

Data science helps understand when to repair a system or machine and prevents unnecessary expenditures by predicting how frequently maintenance should be done.

Root cause analysis

Data science digs deeper into the causes of high failures and understands why systems malfunction occasionally. It also helps suppliers deal with the potential supply of materials accordingly.

Reduce unnecessary downtime

Predictive maintenance is required to predict if an ad when systems might malfunction. A prior data science analysis only helps in lessening the risk of unforeseen disasters.

Efficient planning for smooth operations

Data science ensures that there is no time wasted in fixing systems that are not vital or replacing equipment that has no usage. This way it plans labor efficiently and also ensures that the operations of the business run smooth.

A course in predictive maintenance and building models is an interesting choice for professionals enthusiastic about pursuing a data science career.

How Can You Choose The Right Programming Language For Data Science?

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How Can You Choose The Right Programming Language For Data Science?

Data Science has made its mark among the most popular programming languages of this era. In a rapidly growing tech-heavy industry, the demand for data science professionals is only increasing. If you are looking for a data science career, programming expertise is a necessity apart from analytical and mathematical skills.

However, before you zero in on your choices for the programming language required for a job, you need to know about the various types of the programming language you can pursue a course in to become an expert in data science.

Python

A highly popular and dynamic programming language, Python is extremely prevalent among data science enthusiasts. It is also among the easiest languages to master, and its capacity to sync with Fortran or C algorithms only increases its demand among your professionals. Additionally, as data science, machine learning, predictive analysis, and artificial intelligence make ints foray into regular jobs, demand for professionals skilled in Pyhton is constantly increasing. If your interests lie in data mining, scientific computing or w development, Python is what you need to learn.

R

If you have completed a basic course in data science and now want to excel in a particular language that helps you with statistically oriented jobs, R is your best option. This might be slightly difficult to master as compared to Python, however, if the statistical analysis is your calling, R is your key. However, R is less of a general-purpose language used for programming, hence, you should pursue R only if you are interested in statistics and data analysis. The additional advantage though is, R can help you deal with linear algebra, even complex ones.

SQL

A mandate for any skilled data scientist, SQL or more commonly known as Structured Query Language, retrieves data from organized data sources and is the most used database language. SQL manipulates, updates and researches into existing databases. Any expert data science would require to pull out and analyze data from the database; this is exactly where your knowledge of SQL will fit the bill. Also, owing to its simple syntax, SQL is among the most readable languages in data science.

Javacou

If your interest lies more in learning a general-purpose language, Java is your answer. Supported by Oracle, Java is a unique computing system that makes migrating between platforms easier. Also, Java is widely used among organizations to create and launch mobile or web applications. If you are a skilled software engineer, developing engineer or software architect, Java will help you make the most of learning programming stack.

Scala

Next on the list is Scala, highly popular as a programming language with an immense user database. If you are interested or have to eventually wok with data sets that are really heavy and high on volume, Scala will help you nail the functional bit along with the strong static type bit as well. Scala is an open-source and general programming language, that can be operated within Java or JVM itself. Scala is your best option when it comes to working with processor clusters and Java codes.

SAS

Very similar to R in terms of usage, SAS is also used for statistical analysis, though unlike R it is not an open-source programming language. Noted to be among the oldest language used for statistics, SAS is highly reliable and often finds its use in predictive modeling, business intelligence, and complex analytics. Organizations keen on using a secure and stable platform for their analytical needs mostly use SAS since it offers a variety of packages that help in statistical analysis and machine learning.

Conclusion

While learning any of the above-mentioned programming languages will help you make the most of your data science career, if you are more enthusiastic and want to climb the career ladder faster, it is always advisable to go for more than one language. This not only gives you flexibility while changing jobs but also makes you a skilled professional.

The Common Data Science Interview Questions To Remember..!!

Posted on by Imarticus Learning
Reading Time: 3 minutes

Data science interviews are often considered to be difficult and it might be difficult for you to anticipate what questions you will be asked. The interviewer can ask technical questions or throw you off guard with questions you hadn’t prepared for. To pursue a full-fledged data science career, it is important for you to be up to date on an array of questions that might be asked during the interview, ranging from programming skills to statistical knowledge, or even field expertise and plain communication skills.
Here is a segmentation of the various categories along with the list down of the possible questions you can expect in each category as an interviewee during a data science interview.
Statistics
As an interviewee, it is essential for you to be prepared on statistical questions since statistics is considered to be the backbone of data science.

  • What are the various sampling methods that you know of?
  • Explain the importance of the Central Limit Theorem.
  • Explain the term linear regression.
  • How is the term P-value different from R-Squared value?
  • What are the various assumptions you need to come up with for linear regression?
  • Define the term- statistical interaction.
  • Explain the Binomial Probability Formula.
  • If you were to work on a non-Gaussian distribution, what is the dataset you would use?
  • How does selection bias work?

Programming
Interviewers may ask completely general questions on programming to test your overall skills or may try and test your knowledge on big data, SQL, Python or R. Listed are a couple of questions that may turn out to be relevant for you to crack that interview like a pro.

  • List the pros and cons of working with statistical software.
  • How do you create an original algorithm?
  • If you were to contribute to an open-source project, how would you do it?
  • Name your favorite programming languages and explain why do you feel comfortable working in them.
  • What is the process of cleaning a dataset?
  • What is the method you would take for sorting a large list of numbers?
  • How does MapReduce work?
  • What is Hadoop Framework?
  • If you are given a big dataset, explain how would you deal with missing values, outliners and transformations.
  • List the various data types in Python.
  • How would you use a file to store R objects?
  • If you were to conduct an analysis, would you use Hadoop or R, and why?
  •  Explain the process using R to splitting a continuous variable into various groups in R.
  • What is the function of the UNION?
  • Explain the most important difference between SQL, SQL Server, and MSQL?
  • If you are programming in SQL, how would you use the group functions?

Modeling
While a Data Science Course will teach you the basics of modeling, at an interview you may be asked technical questions like building a model, your experiences, success stories and more.

  • What is a 5-dimensional data representation?
  • Describe the various techniques of data visualisation.
  • Have you designed a model on your own? If yes, explain how.
  • What is a logic regression model?
  • What is the process of validating a model?
  •  Explain the difference between root cause analysis and hash table collisions.
  • What is the importance of model accuracy and model performance while working on a machine learning model.
  • Define the term- exact test.
  • What would you rather have; more false negatives than false positives and vice versa?
  • Would you prefer to invest more time in designing a 100% accurate model, or design a 90% accurate model in less time?
  • Under what circumstances would a liner model fail?
  •  What is a decision tree and why is it important?

Problem Solving
Most interviewers will try and test your problem-solving ability during a data science interview. You may be asked trick questions or be subjected to topics that evoke your critical thinking abilities. Listed are some questions that will help you prepare for an upcoming interview.

  • How would you expedite the delivery of a hundred thousand emails? How would you track the response for the same?
  • How would you detect plagiarism issues?
  • If you had to identify spam social media accounts, how would you do so?
  • Can you control responses, positive or negative to a social media review?
  • Explain how would you perform the function of clustering and what are the challenges you might face while doing so.
  • What is the method to achieve cleaner databases and analyze data better?
    For more such articles, feel free to click on the below link:
    How To Build A Career in Data Science?

Data Scientist Profile In 2019 Education And Skills Set

Posted on by Imarticus Learning
Reading Time: 3 minutes

Data Scientist Profile In 2019 Education And Skills Set

A data science career is one of the most sought after in modern times. The harnessing of data has been made possible by advancements in AI, ML, Deep Learning and Neural Networks over the past three decades. And, the very volume of data being generated is so humungous that the term big has become Peta volumes of data and Peta times as big.
The job is not only highly paid, in high demand, but is also very satisfying. Let us then take a look at the education, skills, and attributes required to make a data science career.
The successful data scientists of 2019 look a little like this to those aspiring to be one. According to bigdata-madesimple.com, the typical data scientist is 69% a bilingual male, has about 8 years of work experience of which 2.3 years are as a data scientist. 74% of them have a Masters or doctoral degree and 73% of them are fluent in Python or R. But that is not the whole truth. What if you are fluent in Java and are a female?
There are almost as many PhDs-28 percents to be exact, as there are graduates and lower aspirants who are almost 24.2 percent of the aspirants. This would lead one to infer that a Ph.D. is not essential and it is the skill and abilities that count for more than just the degrees. You could land a job with an IT background if you are in that 9 percent of the cases or land an internship in 8 percent of the cases too.
The main contributors serving as the magic doorways were experienced in the field of consultancy services in 6 percent of the cases, from the field of data analysis in 13 percent cases, or from the IT field in 9 percent of the cases. A sizeable 50 per cent also came in with experience as data scientists meaning the offers were more than acceptable in monetary terms to shift jobs. 15 percent formed the other category in terms of their fields of specialization.
The popular educational background subjects were 22 percent from Computer Science, 21 percent from Economics and a mere 12 percent from data sciences. This is probably due to lack of data science degrees or that there is ample scope for academic circles to include this as a subject at the college levels.
Which university you study in may not improve your employability as a data scientist. While 31 percent had studied at the top 50 as per the 2019 Times Higher Education Ranking, 24 percent came from universities ranked at 1001 and more. More than half of the participants had taken online courses with 43 percent having completed at least 1 such course of an average of 3 courses. The popularity of these courses would indicate that aspirants took courses to increase their skills and competitive edge in the job market. Fortunately, the university ranking does not appear to matter when being employed as a data scientist.
Python is the leading preferred data science course among the programming skills globally which is closely followed by R. In India and the USA the skills in R and Python are both valuable unlike in the UK and other areas where Python led the charts. About 70 percent of the data scientists in 2018 had previously worked in the tech industry. In 2019 just 43 percent were from the tech industries and 57 percent from other industries and the financial sector.
Country-wise statistics show that the industrial sector in the UK recruited more data scientists than the tech segment which is not the trend in other areas. The normal pattern is broken by India in terms of it having fewer PhDs and larger numbers of graduate data scientists. The USA has the least number of new hires in the data scientist role compared to other countries and the preference for Python as the choice of a programming language is slightly higher in the non-Fortune 500-list of firms.
Conclusions:
The survey of the data definitely indicates that the data science career is one of the best for career aspirants. It also indicates that your interest in acquiring the skills is very crucial to your achieving the task. Training institutes like Imarticus Learning are at the forefront in turning out wholesome data scientists with the skills to fit any employer’s bill of employability. The icing on the cake is that this data-based career is for all aspirants immaterial of educational background, degrees, sex or location. Reach out to Imarticus today. Hurry!

Why Do Data Scientists Need To Learn Java?

Posted on by Imarticus Learning
Reading Time: 3 minutes

Java has today regained its prominence as the most popular language suite for developers and has outrun both R and Python. This is not surprising since Java boasts of the largest community of developers and also has applicability, compatibility, and ease of learning to aid it. AI, ML, and data sciences are all relying on the JavaScript suite and its applications and these are the areas seeing rapid evolution and need for personnel.

Further, when demand rises the payouts get better. Career aspirants and career-changers both are ready to learn data science and are flocking to these fields and this only adds to the popularity of Java as the ultimate weapon in the developer’s kit.

Here are the top reasons to learn data science and Java.

  1. The old-gold class: Being the oldest language in enterprise development it is frequently found that legacy systems have their infrastructure already running on Java. This means you have probably used R or Python for modeling and have to rewrite the models to suit the system running in Java.
  2. Wide frameworks: The Big-Data tools and frameworks like Flink, Spark, Hive, Hadoop and Spark are Java-based. Familiarity in the Java-stack is thus easier for analysts working with large data volumes and big data with Hive and Hadoop.
  3. Libraries aplenty: Java has toolsets and a great variety of libraries for ML applications and data science applications. Take a look at Deeplearning4j, Java-ML, Weka, or MLlib to quickly resolve and issues in data science.
  4. REPL and Lambdas: While Lambdas that came with Java 8 altered the verbosity in Java, the recent REPL of Java 9 adds iterative development to the developer kit. It is now easy to learn and work in Java than it initially was.
  5. Virtual Machine in Java:  JVM helps write multi-platform identical codes facilitating rapid customization of the tools required. With the IDEs variety on offer, developers can be more productive.
  6. Strongly Typed: This does not refer to classic static typing. Rather it deals with Java being able to specify the types of variables and data the developer needs to work with. The strong typing feature is especially useful in large data applications and is a feature that is well-worth the developer’s time in avoiding trivial unit test writing and in maintaining the code base of applications.
  7. Scala in JVM: Heavy data applications make learning Scala easier when you code in Java. The Scala framework is awesome since it provides data science support and other frameworks of the likes of Spark can be built atop it.
  8. Provides jobs: Other than the SQL requirements, it is Java that is most popular in the job-space as per the chart indexed below. All the more reason to learn data science and Java for developers!
  9. Scalability: Application scaling in Java is rapid and excellent making it the developer’s choice for writing complex and larger AI ML applications. Especially so if you are writing the program ground-up since then you only need the one language of Java coding.
  10. Speed: Java is fast and provides for fast integration in heavy large-scale applications. The likes of LinkedIn, Facebook and Twitter rely on Java for heavy data engineering.

A data scientist/ developer is the one who is the single point of contact for the data itself. They take the data both structured or unstructured and use a wide variety of engineering, statistical, mathematical, and programming skills to spot trends and arrange the data organizing and managing the data to resolve the targeted outcomes. In essence, they are the people the analysts look up to for the data they need to analyze.

Practical skills required:

Let the truth be told, even if you do your master’s or a Ph.D., to be a good and effective data scientist you will need to also garner training for technical skills in:

  • Proficiency in social sciences
  • Programming in R and Python
  • Coding and writing with the Java suite
  • BigData querying  on Hadoop framework
  • Coding and SQL-Databases
  • Apache Spark
  • AI, ML, and Neural networks
  • Visualization of data
  • Working with unstructured data

You could also bolster your knowledge in managing data through online MOOCs, tutorials, and courses. Ensure your training partner for paid courses is a reputed institute like Imarticus Learning as they offer to train you for professional certifications and also award certifications that are valued in the industry.

Conclusion:

If you’re an analyst, Data Scientist, Deep Learning or ML Engineer the Java skill quotient is worth improving when you are eyeing lucrative and in-demand development jobs. You should learn data science and Java at Imarticus Learning if you want to stay ahead of the job-curve.

For more detailed information regarding this and for further career counseling, you can also contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Hyderabad, Delhi, Gurgaon, and Ahmedabad.

Good Ways to Learn Data Science Algorithms, if Not From IT background?

Posted on by Imarticus Learning
Reading Time: 3 minutes

At the beginning of your career in data sciences, algorithms are hugely over-rated. Every routine task, every subroutine, every strategy or method you do or write is because of an effective algorithm. In essence, all programs are formed of algorithms and you implement them with every line of code you write! Even in real life, you are executing tasks by algorithms formulated in your brain and just remember that all algorithms are simulations of how the human brain works.
Just as you begin with baby-steps and then worry about speed and efficiency it is a good routine to start your Data science career with the algorithms if you are not from a computer science background. And there are hordes of resources online that you can start with. Some people prefer the Youtube tutorials to reading books or even a tandem process including texts and videos which is fine.
As a beginner of a Data Science Career, your focus should be on making your algorithm work. Scalability comes much later when you integrate writing programs for large databases. Start with simple tasks. You will need to learn by practice and with determination laced with dedication. Don’t give up, as you never did, when you started walking or talking in English!
At the onset of learning, you will need to:

  • Understand and develop algorithms.
  • Understand how the computer processes and accesses information.
  • What limitations does the computer face when executing the task on hand?

Here’s an example of how algorithms work. Though huge amounts of data are stored and processed almost instantly, it can process/access only one/two pieces of information every time. This is the basis that algorithms use for simple tasks like finding the lowest/ highest number. An algorithm is essentially a series of sequential steps that helps the computer perform a task.
Starting with very basic algorithms for finding maximum/ minimum numbers, identifying prime numbers, sorting a list, etc will help understand and move to more complex algorithms. Modern times computer scientists use the suite and libraries of optimized and developed algorithms for both basic and complicated tasks.
For one who is not from a computer science background here are the basic steps to learn algorithm writing.

  • Begin with basic mathematics needed for algorithmic complexity analysis and proofs.
  • Learn a basic computer language like the C Suite.
  • Read about data science topics and the best programming practices:
  • Study algorithms and data structures
  • Learn about data analytics, databases and how the algorithms in CLRS work.

Learning algorithms and mathematics:
All algorithms for a  data science career requires proficiency in the three topics of Linear Algebra, Probability Theory, and Multivariate Calculus.
Some of the many reasons why mathematics is crucial in learning about algorithms are: 

  1. Selecting the apt algorithm with a mix of parameters including accuracy, model complexity, training time, number of features, number of parameters and such.
  2. Selecting the validation of strategies and parameter-settings.
  3. Using the tradeoff of Bias-Variance in identifying under or overfitting.
  4. Estimating uncertainty and confidence intervals.

Can you learn Math for data science quickly? The answer is that it is not required for you to be an expert. Rather understand the concepts and applications of the math to algorithms.
Doing math and learning algorithms through self-learning is time-consuming and laborious. But, there is no easy way out. If you want to quicken the process there are short and intensive training institutes to help.
While there may be any number of resources online, mathematics and algorithms are best learned by solving problems and doing! You must undertake homework, assignments and regular tests of your knowledge.
One way of getting there quickly and easily is to do a Data Science Course with a bootcamp for mathematics at Imarticus Learning. This will ensure the smooth transition of math and algorithmic data science applications. At the end of this course, you can build your algorithms and experiment with them in your projects.
Conclusion:
Algorithms and Mathematics are all about practice and more practice. However, it is crucial in today’s modern world where data sciences, AI, ML, VR, AR, and CS rule.
These sectors are where most career aspirants are seeking to make their careers because of the ever-increasing demand for professionals and the fact that with an increase in data and development of these core sectors, there are plentiful opportunities to land the well-paid jobs.
At the Imarticus Learning, Data Science career course, you will find a variety of courses on offer for both the newbie and tech-geek wanting to go ahead in his/her career.
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What Do Experienced Data Scientist Know That Beginner Data Scientist Don’t Know?

Posted on by Imarticus Learning
Reading Time: 3 minutes

The one thing that sets the experienced data scientist from the beginner’s data scientist career is that 99 percent of data sciences lies in the effective use of story-telling!

At the start of one data scientist career, most have the same skill-set as the top scientists with many years of experience and are job-prepared. The best of them learn to use their tools and techniques gained with practice and expertise to become excellent at using data to tell a compelling user-story. A data scientist in the early stages of the career is actually practicing as an analyst of data and probably comes from any of these fields. Namely,

  • Data analysis and wanting to pursue academics.
  • Analysts on the business intelligence side.
  • With computer science, statistics or mathematics expertise.

Large doses of the previous job role are normally used at the beginning of the data scientist’s jump into this field. That is being job prepared! And it will not be uncommon if the analysts are the busy rattling of their insights on blodgets and widgets, the business intelligence or business analysts present information in complex tables and graphs, and the group of CS, mathematicians, and statisticians write code the whole day. But that is not what a data scientist’s role is about especially in this role.

Whether you have deep learning knowledge, can crack ML algorithms, or write compelling codes for vector classifiers the skills you will need to be an excellent data scientist are not the same as the skills you landed the job with.

Your job is to use the data to tell the most compelling story while using your skills, tools and techniques learned to graphically illustrate your narration. Compare your story to a thrilling novel that you can’t put down till the last page. Your tale has to be anchored to the data and last till the final calculations are presented.

Story-telling skills:

For this, you will need the following skills they did not teach you in college and comes with aptitude, practice, and experience in a Data Scientist Career. Let us explore these attributes.

  • Structure: This is the manner of presenting data and information in an easily comprehended, logical, no-nonsense and understandable way that any reader or user can relate to. That’s precisely why most storybooks introduce their characters in the first few chapters itself. Most people err in not defining the issue and pitching its solution at the very start of writing.
  • Theory of the narrative: Good stories sustain interest till the very end and that is the essence of the narration. Keep your lines tight and use your data findings to get the story across cogently.
  • Expressive writing: This is the essential glue that holds the interest, tells the narrative and proves your point clearly and without ambiguity. Your grammar, sentence construction and choice of apt terms and words will go a long way and comes only by practice. Whether it be an email, a press note or internal communication remember that it may land on the table of the management head or your juniors.You wouldn’t want spelling and syntax errors in your calculations or writing style. Avoid ambiguous terms, technical jargon, and irrelevant information. At the beginning all tasks are difficult. They do ease out with regular practice and learning the right way to do things.
  • Presenting complex information: Being a data scientist isn’t totally about writing those accurate reports. As you move up the ladder you will be asked for your views, suggestions, and assessment. These are of a highly complex and technical nature and you need to train yourself to present your views without compromising accuracy, truth or the crucial data supporting your premise.This needs a lot of practice in all the above attributes to reach a level of credibility coupled with all the essentials and ingredients of the story. If you fail here you are possibly doomed to remain in those middle rungs of your career and can never rise to the top. Wisdom and skill are not gained by the number of years you spend on the job. They are learned on the job with regular and dedicated practice.

Conclusion:

The difference between the artist and artisan is the situation that occurs in the Data Scientist Career. No matter what your background is, excellence at the data scientist’s job comes from practice and learning from experiences. In data sciences, you will not only have to acquire the right tools of the trade, but you will also have to excel at wielding them artistically to tell the story WITH data. Not tell the story OF data.

At Imarticus Learning the data scientist learns this during the training in the soft-skills and personality development modules. Begin your story-telling today!