Data has transformed its existence into an impactful resource which could benefit anyone who has it in hand. The use of data today has created multiple impacts in the lives of numerous businesses. With data a brand or a business can easily identify the right target audience, tap them with effective solutions and easily convert them into a sales leads without any doubts of uncertainty.
The impact of such a powerful solution has driven the role of data scientists in a similar manner. Data scientist considers data as an asset which can be cracked open and analysed so that brands can benefit from the advantage of conducting efficient decisions plans which will turn their struggles of capturing leads into quality sales numbers.
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Do you share an interest in the world of data just like your peers? Do you see yourself exploring into the world of data to create solutions which matter? Are you seeking a solution which will help you to begin your journey of becoming a data scientist?
You have reached the right place because by the end of the article, you will just not learn about what is data scientist like how other articles share their information, in fact you will be more affirmative and confident in choosing this career path.
Understanding how crucial this, we interviewed a few experts from this field to give you better clarity on the topic. We had asked them to share their insights about the world of data science according to them with the questions listed below:
- Ethical standards to follow for data science
- The data science revolution across industries and society
- How web scraping actually helps in scraping data?
- How to Collect large sets of structured and unstructured data from different sources
- How to Clean and validate the data to ensure accuracy, completeness, and uniformity
- How to Interpret the data to discover solutions and opportunities
- How data scientists use proxies to web scrape and collect huge data
- Important tools used
1 . Elias Lankinen, from deepaz.ai stated,
Data scientists’ job is very demanding because they are often in charge of important data and/or algorithms. They need to be knowledgeable about cyber security and in general how to handle the data to not give it to the hands of bad people but they also need to have certain ethical standards.
It’s not okay to share sensitive customer data to third party, ML algorithm can’t differentiate people based on gender (like Apple might have done https://edition.cnn.com/2019/11/12/business/apple-card-gender-bias/index.html), or customer information is always the number one priority are great examples of ethical standards data scientists need to set for themselves even though law doesn’t cover everything.
Data scientists are needed more and more every day in different industries because they can get information from data better than anyone which makes it easier to do business decisions.
Even companies like McDonald’s (https://www.wired.com/story/mcdonalds-big-data-dynamic-yield-acquisition/) are betting on this because they understand how valuable information gathered from data is. In the future people will do more choices based on data and less intuition. Even now data scientists work really closely with CEOs or other executives who make decisions. Sometimes they get information about data but doesn’t take actions saying that it’s wrong or something which is a problem right now. Over time they probably learn to listen more data and less their own intuition. Of course sometimes data scientists might have messed the plot or data and the information is really wrong but that is why it’s good to always have some knowledge about this to check weird results.
Okay how data scientists then get the data in practice. Data scientists in bigger companies often get most of the data from companies’ own databases. Nowadays every company that’s targeting to over a billion dollar market cap should collect everything they can. It doesn’t matter if there is no use case for the data right away. Saving data is nowadays easier and cheaper than it used to be. Data lakes have gathered popularity by offering a simple way to collect any kind of data. The idea is that company can store any type and size of data to data lakes and then later data scientists can use it wherever they want.
Smaller companies need to do different things to get more data and web scraping is probably the biggest source of data to many smaller companies. Sometimes companies can scrape data from competitors but more often companies get the data from other kind of websites like Wikipedia. Airflow is nowadays pretty common tool used in this process. Websites try to avoid web scraping because in big amounts it causes cost to the company and might make the website slower for users. With proxies it’s possible to avoid these websites noticing web scraping. It basically connects to the website via another server that hides the original computer’s information. Again data scientists need to think at this point what is ethical. Often making multiple calls to a server in a second is seen as harmful. It’s better to add some slowness to the scraper to make sure the website can handle the scraping and the harm is minimal.
As mentioned above, it’s pretty common that nowadays companies gather whatever they can and then shovel it to data lakes without caring much about the structure. This way the job of data cleaning moves to data scientists. First of all they need to pull the different kind of data from data lakes or other storage to one place which might be local database. This is where they combine it into simpler format and remove the information they don’t need. They also need to check things like date formats the same or is there clearly incorrect data. This is called data cleaning. It’s around 60-80% of their job depending where they work.
Then the other part of their job is to use this data. In data cleaning process they often get to know the data better by plotting correlation plots or making decision trees. After they are familiar with the data they can either show the results to other people in the company or create products for customers.
2 . Sharad Varshney, CEO of OvalEdge stated,
1 . Ethical standards to follow for data science:
Due to the progress of innovations in data science in algorithmic trading, self-driving cars, and robotics, the distinction between human and artificial intelligence is becoming increasingly difficult to distinguish. Today, sophisticated algorithms are replacing humandecision-making and artificial intelligence (AI) systems make more decisions.That introduces several ethical considerations about privacy, lack oftransparency, bias, and discrimination, as well as the lack of governance andaccountability. Business and social organizations often rely on complex AI-based algorithms that use increasingly complex methodologies and models that it’s practically impossible to explain precisely how they work.
Can we trust their results? What if there are some “secret criteria” that influence automated decision-making? The inherent logic of AI-based platforms can be gamed, and that creates opportunities “to cheat” the system. So who is legally responsible if unethical practices emerge? That’s why it’s important to develop ethical standards of data science and create governance structures to monitor the ethical deployment of AI and educate developers, data architects, and users on the importance of data ethics specifically relating to AI applications.
2 . How to Clean and validate the data to ensure accuracy, completeness, and uniformity?
Data Cleaning is essential to ensure that we achieve high data integrity, which allows us to make the best possible decisions. That’s why it’s crucial to develop and implement a robust data cleansing strategy plan, taking into account the big picture as well as your unique situation (your goals and expectations,current challenges, etc.). You should start by developing a data quality plan and create data quality KPIs. The next step is to standardize data at the point when they are initially captured. You should create a standard operating procedure for your team.
This ensures that all information is standardized when it enters your database and will make it easier to catch duplicates,inconsistencies, and inaccuracies. When validating data, you need to assess its accuracy and consistency, by comparing it to another accurate source. It’s always better to perform validation of data in real-time when the data is initially captured because, in this way, we can significantly improve the overall quality of the data sets.
Unfortunately, if you deal with large messy datasets, 100% validation is impossible, so it’s essential to have realistic goals.
3 . Alex Bekker, Head of Data Analytics Department at ScienceSoft, an IT consulting company stated:
1 . The data science revolution across industries and society:
The rise of artificial intelligence and deep learning, which are currently the most advanced forms of data science, has enormously extended the list of business tasks that companies from different industries can solve more effectively than ever. Conducting automated visual inspections, optimizing inventory, forecasting demand, assessing and managing risks are just a couple of tasks that can be powered with data science.
To get more details on how data science helps optimize inventory, you can read my recent article: Inventory Optimization Headache and How to Approach It with Data Science.(https://www.scnsoft.com/blog/inventory-optimization-with-data-science)
2. How web scraping actually helps in scraping data?
Companies can make good use of both internal and external data sources, and web scraping helps them get valuable external data (like prices taken from the competitors’ websites, product reviews or brand mentions retrieved from social media).
4 . Krzysztof Surowiecki, CEO at Hexe Data and a professional Data Analyst holding 16 years of experience in online technologies, business and legal issues that condition the functioning of entities in the “e-economy” stated,
I think that in reality, we are looking in the data for an explanation of reality or its justification – an explanation is when we want to know the reason for a given thing, and an excuse when the data is to confirm a decision that is already practical (or will not be taken if the data is inadvertent). Thus, data scientists supports the cognitive – decision-making process.
1 . Ethical standards to follow for data science
Reliability and honesty in the field of data handling and presentation. We know that often choosing the right visualization or counting algorithm significantly changes the data context and allows you to manipulate the recipient. The data are to be apolitical, true.
2 . The data science revolution across industries and society
Data becomes the good of all of us, and each of us has easier access to it. That is the most important change.
3 . How web scraping actually helps in scraping data?
Process simplifier gives access to data for people/organizations with smaller and more modest facilities. However, this creates a risk that such content may be used contrary to the author’s intention – it is therefore very important to respect copyright.
4 . How to Collect large sets of structured and unstructured data from different sources?
Depending on the budget, we can either use cloud solutions or choose our own infrastructure – specific solutions depending on the type of data. Of course, when it comes to unstructured data there is more work that needs to be done for such data to have value in use. If the data has no structure, then it needs to be given this structure. In addition, as a rule, it still needs to be cleaned.
5. How to Clean and validate the data to ensure accuracy, completeness, and uniformity?
Depending on the level of our knowledge and the size of the database, we can use solutions based on programming languages (python or R are perfect for data validation) or use box solutions such as Tableau Prep. Data scientists often have their go-to tool but it always depends on the project.
6. How to Interpret the data to discover solutions and opportunities?
The most important thing data scientists should do is to look for correlations, look for trends and relationships between data. BI tools such as Tableau or Power BI are perfect choice to do so.
7 . How data scientists use proxies to web scrape and collect huge data?
A proxy is a third party server that allows you to route your request through their servers and use their IP address. When using a proxy, the website you make the request to no longer sees your IP address but the IP address of the proxy, which gives you the ability to scrape the web with higher safety.There are 3 main types of IPs to choose from: Datacenter IPs, Residential IPs, Mobile IPs.
8. Important tools used
Let’s start with the basic tool which is excellent knowledge of Python and tools like numpy, pandas, seaborn, etc. Always remember to choose the most suitable tool for the project.
5 . Evan Ankley, Owner of sportsbookscout.com and also a data analyst by trade with 5+ years experience in data science tools stated his opinion,
1 . Google Analytics
Everyone needs GA so they can accurately measure their Marketing efforts as well as user behavior. For example, I have used Google Analytics to see where most people are landing on my site so I can better optimize the experience. Going deeper, I can also analyze what people are clicking on and where they are falling off in the funnel.
2 . Looker
Looker is a standardized data visualization tool that puts the data in the hands of anyone in the company. Looker is best for having one source of data and allowing anyone to be able to pull information and easily visualize it to get quick answers.
1 . Tableau
On top of GA, having a data visualization tool like Tableau is a must. Many times you will need much deeper data than what Google Analytics can provide on the surface. You can plug in your database to Tableau or even use data from Google BigQuery to mine more insights. Tableau is great for doing really deep dives. For example, I have used tableau to segment NFL performance based on various dimensions: location, weather, recent game performance, etc. Tableau allows me to easily visualize this complicated data set.
1 . Snowflake
Snowflake is a cloud database that will house all of your internal data. It is important to have this because it will allow you to more easily access and query your internal data. The biggest benefits I see with Snowflake are the speed at which you can query large datasets as well as data processing. Snowflake gives you the opportunity to view your internal data in near real time, which is incredibly important for some time sensitive initiatives (ex: promotions).
6 . Fabricio Matzinger from StoneAlgo stated,
One new tool I have recently found out about is Google’s Data Studio. It is a great way of scheduling reports/analysis and presenting it neatly. As opposed to having to pull data every time you want to do a report, you simply link it to Google Analytics or Adwords, create the data outputs and analysis you want to show and then schedule for an automatic weekly delivery. Highly recommended!
7 . Deeana Radley from Technology Evaluation Centers stated.
I invite you to view TEC’s top database and data analysis software categories:
Technology Evaluation Centers is an impartial, leading software selection firm, trusted by businesses for over 25 years.The pages list user reviews, include detailed overviews of the product, allows users to compare solutions back-to-back, and has a helpful buyer’s guide.
8 . Raheel, Cofounder of Mltrons revealed,
“Mltrons is a self-serve platform for anyone to create and deploy machine learning algorithms. It is simple, quick and easy. No coding is required — instead machine learning models are created automatically from users own data. Users simply upload their data and tell the system what they want to predict. Automatically, they get the best predictive model with explainable results that they can share with their co-workers and managers to make better data driven decisions.
Here is a challenge that it helped to solve: https://www.mltrons..com/optimize_marketing_revenue.html
Who are data scientists?
Data scientists understand the cruciality of what data can do, they are well aware of how significant a solution can be curated from the data. Did you know that with valuable data in hand, data scientists can easily find solutions as well as create solutions which will be a more profitable and successful solution for any businesses when they apply it.
What started as data analysis and statistics, had now turned data scientist into a valuable asset in the field of data science which any brand wouldn’t mind spending large amounts of money to retrieve powerful solutions from such experts.
But how did data scientist become a demand?
Evolution of data scientists
Statistics and its models are well known in the field of data science. From starting as a data analysis and statistics approaches, data science today includes more than just these factors. With the businesses and industries growing digital, data science is now practising approaches which include machine learning, artificial intelligence and internet of things.
Data is no longer a solution which would require just identifying it, it now holds the essential factor to solve and create expert solutions which could turn the way a business grows and conduct its lead capture activities. From resolving prospects behaviour pattern, businesses understood that with a lump sum of data available, chances are innovative solutions can be retrieved which will revert in good results or outcomes.
For instance, say if the data you have in hand can help you find an innovative solution on how you can conduct lead generation activities in a shorter period, wouldn’t you invest your focus and amount on such an asset? That is exactly the power data science and the ones who conduct it, data scientist holds.
So which areas exactly can you find data scientists functioning the most?
What areas do Data Scientists specialize in?
Data scientists specialize in six essential areas of data science:
1 . Investigations- With the help of vital tools, data scientists collect information which will help them in conducting their research and solving complex problems with better clarity
2. Selection of right models and methods of data retrieval- Not all methods will go hand in and with the wok data scientists conducts. Hence with their knowledge and the growing time frame, data scientists will acknowledge which tool will be most suited for different workforce situations
3 .Working with brands- To help a brand grow better, data scientists will indulge with them to gain as many insights as required for them to conduct their activities. With continuous engagement, data scientists will be able to collect and analyze relevant data which matters
4. Incorporating the right tools- Data scientists deal with multiple projects. Each of those has a huge data situation in hand. In order to scatter through the heap of information and find the relevant data, data scientists make use of essential tools or software which can help them to analyze their work more easily
5. More theory form- The world of data science revolves around huge heaps of data in theory form. To assist such a process data scientists to have countless applications to cater to
6. Evaluation of essential tools- A tool six years back may have work wonders then but may not be effective in the current market. Hence data scientists revolve around the use of the latest tools to measure the right effectiveness which will benefit their work better.
7 . Having an interest in data scientist doesn’t completely accommodate all the requirements needed to become one, hence acquiring the right skills can benefit many potential students like you to walk into the career field with the right direction.
Relevant skills needed to become a better data scientist
1. Knowledge of R programming
Potential learners looking to seek a career in this field must have a good knowledge of R programming. This is one of the most widely used programming tools ‘’In fact, 43 percent of data scientists are using R to solve statistical problems.’’ The main agenda for using R is because it is responsible for solving any kind of problem occurring in data science.
2. Eager to learn more
Data scientists deal with data and as mentioned earlier the market keeps fluctuating. So with the change in time, the data changes. To become a great data scientist, analyzing data shouldn’t just be your limitation. You must enhance your knowledge each day. Keep reading and updating yourself with new information because when you are aware of what’s happening it becomes easier for you to analyze the solutions to many situations.
3. Having an understanding of the industry
Collecting and analyzing data without first identifying the purpose of doing does not seem right. A main role for the data scientists in the making is to first understand what industry they are indulging in, what is the status, what had the industry worked before, what is happening in the industry and also keep in mind that for every problem that you are identifying or every solution you are creating ensure that it serves a purpose for what is happening. It should be beneficial to those who you are helping.
4. Enhanced communication skills
Another skill that an upcoming data scientist should master is their communication skills. Collecting data and then analyzing them or finding solutions for complex issues are the job responsibilities of data scientists but there is also another responsibility which data scientists share too, the art of conveying the technical knowledge into user-friendly information. A data scientist must know how to convert the technical words into a story that is easily understandable and interpreted by non-technical users.
5. Having a good team spirit
Data scientists don’t operate singly. They have to engage more with the individuals who are working on the same projects as for them. Sometimes when it comes to helping a brand grow, a data scientist needs to indulge with every member of the team so that they can receive all the insights needed to shape their workflow better. Hence data scientists must know how to work in a team and have the right engagement with everyone so that at the end of the day the contribution is not just witnessed by them alone but of the whole team.
6. Thinking from the point of view of a business
You could have a brilliant knowledge on the skills displayed above, but if you don’t understand how you can cultivate them from a business front, the efforts you put it is useless. Any data scientists need to understand how their current actions can benefit a business or how can one move of theirs make an impact in the way a business can receive profitable returns. This factor is important as it helps to create a framework of what needs to be achieved.
7. Catering into the deepest data provided
Do you know what makes a data scientist to really stand out from the crowd? The fact that they can see few data solutions even when its not visible in the first view. Data scientists can dig in deeper and identify how a particular data can actually help a business. Many times not everyone will be able to identify that, they will only spot the top layers, but for a data scientist, they dig in through the deepest layers, ‘’perceiving patterns where none are observable on the surface and knowing the presence of where the value lies in the unexplored pile of data bits.’’
Apart from these basic skills, it is also necessary for the upcoming data scientists like you to develop the right technical skills which can help to conduct efficient data activities.
What technical skills a data scientist should incur?
1. Python Codes
Python is considered as one of the most practiced coding languages in data science. ‘’ 40 percent of respondents surveyed use Python as their major programming language.’’ It is considered as one of the finest codes and is used for every step in the data science process. From carrying multiple different formats of data to creating valuable datasets, having a piece of good knowledge in such a code is a plus point.
‘’3490 LinkedIn data science jobs ranked Apache Hadoop as the second most important skill for a data scientist with 49% rating.’’ Hadoop may not be used often but it emphasizes its usage on the critical situations in data science. For instance, say the data being stored is too much for the memory capacity being held or if you have to transfer data to different servers Hadoop can be quite helpful here. It plays a great role in conducting data filtration, exploration, sampling and any kind of summarization. Having a piece of knowledge about this tool can be helpful.
3. Unstructured Data
Unstructured data isn’t easy as it includes activities that cannot fit into any database table. But cracking this can win you a great advantage while interpreting data. The reason why unstructured data is in a complex form is that all the data is just dumped together in one corner, because of this it becomes extremely difficult to crack it. Unstructured data is essential when it comes to concluding. Hence understanding how it can be managed and organized can help upcoming data scientists to earn a good skill in their career.
4. Visualization of data
Another important factor for data scientists as mentioned earlier in the process of converting all the data into a visual form. Not many users are knowledgeable about the coding language or the words hence it is important that the data scientists know exactly how to create a visual form of the data curated so that when any user reads it they can understand what is being conveyed to them. Work on storytelling and chart/graphic creation for displaying better technical language.
5. Keeping up with AI (Artificial Intelligence)
Today many companies are using the help of AI or machine learning to engage and conduct better business activities with their prospects. With AI growing, there are still many data scientists who are still lacking in such knowledge. Learning and implementing machine learning or AI tactics can help majorly in data science activities as it helps to solve complex issues that form a prediction based on the organization’s outcome results. AI is growing and it is wiser for you to learn more in-depth about it and how you can implement it better with your workflow.
SQL stands for structured query language and it is a vital process data scientists should know about. SQL helps to conduct essential functions such as add, delete, extraction of data as well as help to conduct analytical. Modifying database patterns is also one of its key qualities. processes. With SQL, data scientists should be able to write down as well as execute any complex queries in it. It is vital to learn this process as it has a lot of benefits, a few being:
- Reducing the time required to solve difficult issues
- Lets you access data, communicate with it and work on making it better
- Enhances a data scientist profile for future growth
- Helps to understand relational databases much better
Understanding the requirements needed to become a data scientists, now let’s move on to understanding what your role actually does.
What do Data Scientists really do?
At Nudge, they had explained this question by interviewing their own inhouse data scientist, Dr. Zoe Katsimitsoulia.
When asked about her education qualification, Dr. Zoe stated,
‘’I started with an undergrad at McMaster (Hamilton) in biochemistry, with a specialization in molecular biology & genetic engineering, but also ended up taking a few courses in computer science. I ended up completing a Masters at the University of Liverpool, which eventually lead to a PhD at Oxford in computational biology. I then continued my work with a post-doc at Columbia University in computational biology. ‘’
She continued to state,
’’The programs I’ve looked at have been quite variable in their definition of data science as reflected in their curriculum and in such a short time frame can only provide a superficial treatment of the field at best. This could lead to problems in the future, as the demand increases for this role in business – and you have a workforce with entry level skills.’’
Answering the question about the meaning of data science, Dr. Zoe claimed,
‘’Data science currently is really a continuum being defined by businesses trying to optimize and drive improvements.Data science can come into play when we have complex systems generating lots of data we wish to take advantage of. That means more than just analyzing the data. It means building models using state-of-the-art algorithms to explain or predict behaviour. These models need to be testable and this is where the scientific process comes in. So the major difference between data science and data analysis, is the math and then mentality.’’
Giving an example to the context above she explained,
’I may have a hypothesis that by using profile information I can build a program that recommends content based on explicit and implicit interests that is better than a random selection. There may be multiple variables to consider, and the data or lack of data may create challenges. However you can model this system of people, profiles, interests and content using probabilistic algorithms, clustering techniques, and collaborative or content based filtering approaches, to name a few options.’’
Continuing her interview she replied to the following questions which provided more clarity on data science and scientists:
1 . What Types of Businesses Need Data Scientists?
‘’Well really every business can take advantage of it. There is however this big expectation on the data scientist to transform a business, but really it is much more of a collaboration between the data scientist and the business leaders.’’
2. So Where Do You Find Data Scientists?
‘’A lot of scientists have jumped ship and left academia like I did. The reason (for me) is that in business you experience much more immediate feedback than in research – and that can be very rewarding. Also the money is better :).’’
3. What Do You Think the Future Will Be for Data Science?
‘’ I think there is a lot of great work being done in fundamental research in academia, but also business has driven a lot of the progress and innovation in the field. Companies like Google, Yahoo, Facebook and Linkedin have huge amounts of data they can apply science to, to help both themselves and their users. Ultimately I believe the discipline needs to be better defined, and have more distinction between different types of data scientists. This will really help generate more specific roles, with the needed skills that add real value to businesses.’’
What data scientists really do is determined in their process which is explained below:
Step 1: Identify the problem
Before a data scientist can reveal a solution, it first needs to identify what the problem is actually about. For instance, say if you want to solve the problem of capturing only qualified leads from the bunch of leads you get, you need to explain your data scientist how it affects your business. When data scientists completely understand what the issue is, that is when they will be able to create a direction which will help them solve that problem.
Step 2: Collect valuable data to assist the problem
Once the problem has been identified, data scientist will move forward to collecting the data which will help solve this issue. Since the data available is huge, data scientists will scrape through and take only those data which matters in this process. Incase if the data is already available, data scientist will check if the information is sufficient or it requires some more resources.
Step 3: Begin the data process
When you receive data, not all that is there in your hand will be of added value. There will be many data which is lost, corrupted, bad or not useful. Hence before a data scientist can even begin to analyse the results and create or identify a solution, it is necessary to have clear data in hand which will be efficient while analysing the solution.
Step 4: Get deeper with the data in front of you
Once the clean data is right in front of you, it’s time to dig in deeper with it. Exploring through the data you need to find the cords which can help to assist you in creating the solution required. You will be able to create graphs, and representations of the possible approaches which can work to solve the problems
Step 5: Conduct data scientists activities (analysis)
Here is when the processes effectiveness is shown. Data scientists will be able to place all their approaches and start creating predictions and possible outcomes to ensure that the problem being solved has a reasonable solution.
Step 6: Reveal the outcome
Lastly, the main feature here is to convey all the technical solutions into an easily understandable manner. For data scientists, you must be able to convey the solution in the form of storytelling where the other user listening to it should be able to understand what you are speaking. For instance, if you are going to speak in a technical contact a user may not be able to quite catch their attention to understand what you are speaking. But if you are able to explain the process in a story form, the user will be able to understand the problem and why the solution you are suggesting is beneficial for them and the problem.
To conduct the above process, data scientists should have the right knowledge to commence it which is exactly why the right educational background can add an extra benefit for you to understand and function as a data scientist.
What are the educational requirements to become a data scientist?
To be data scientists, you need to have a bachelor’s degree in either Computer Science, Social science, Physical science, and statistics. The top 3 fields which many of your peers specialize in order to analyze data are mathematics and statistics, engineering and computer science.
Once you have completed your bachelor’s, you have to pursue a Master’s degree in either Data science, mathematics or any other field which relates to the profession. Apart from this, you can take up courses which can enhance your knowledge much better.
If you want to give data scientists as your career shot, you can try out the below free courses which can help to enhance your skills as well as provide you with the right insights needed to walk towards this journey.
Python and SQL are two such coding languages which are the most crucial when it comes to becoming a data scientist, Codecademy teaches such codes for free so that individuals can you can understand the value of such a learning and apply it during your data scientists career process. This course is absolutely free which is the best part and it teaches you the basics which frames your foundation for the subject better. A duration of 7 hours each will be dedicated for SQL learning and Python, a 25 hours duration will be applied.
2. Learning about Data science with python included
You have decided that data interests you and that you want to take data scientists seriously then Udemy has a free course which can spark the interest in your decision much more. From learning about the basics of data science to understanding how python functions and also receiving insights on the type of jobs the role of data scientists offers, udemy has it all covered for you that too for free. Get this course which has 12 lectures with a dedicated hour of precisely 2 hours, 30 minutes.
3. Get a better understanding of Linear Algebra
Skillshare is sharing a video which will help you understand another essential factor in data science, Linear Algebra. Get a detailed insights on the lesson with a 44 videos consisting of exceptional information which will help you to understand the subject right from the basics. The course costs $15 a month and the 44 videos are a total coverage of 6 hours, 51 minutes.
4. Get insights on Machine learning
Don’t have much time on your plate but are still engrossed into learning about machine learning? Well Udemy has a quick solution for you. Udemy is offering a 3 hours course which covers significant topics such as how AI and machine learning play a massive role in data science. The course cost is $150 and consists of 41 lectures with a total time of 3 hours.
5. Learn Machine learning from the expert
Apart from the above, there is also another website that can help you to learn everything you need to know about machine learning such as what it means, how it works and why it is needed in data science and much more with coursera. To preach such valuable information, the Coursera Co-founder itself will be talking to you. The course is free to audit and for certificates, $79 needs to be paid. The course duration is for 7 hours a week for a set of 11 weeks.
6. Get information about the Data science pathways
Codecademy has a course which can help you to understand how the world of data science operates. Their course if so effective that you can easily learn how you can apply the practices in real life. Developed by data scientist, the course covers all the relevant factors needed to become a data scientists today. The course costs $19.99 a month and and is self paced.
Coursera is offering a specialised course in data science where it details out everything you need to know about the world of data science. From framing the right questions to developing the soft skills required, this course will convert a beginner into an advanced data science professional with this course. The cost of this course is $ 49 a month and has 10 courses where the duration is between 3-6 months.
Udacity is offering a ‘nano degree’ course where it gives users the ability to share such a platform by learning from experts other similar like minds. Get solutions to any of your queries and always stay on the direction. The course price is $50 a month with the course duration being 10 hours a week over 3 months.
Getting in data science isn’t difficult when you have the right guidance supporting you. To ensure that you don’t miss out on anything, ensure that you prevent the below mistakes from happening so that the path towards data science as your career is smooth.
Avoid these mistakes for a smoother career in data science
1. Putting your focus more on theory
Yes there is theory too in data science for instance studying machine learning and algorithms and much more. Constantly occupying your mind in theory will not only get you bored but also pull your focus and attention away. Hence it is necessary that while you study the theory also enforce a pinch of practicality in it. For instance you could understand how the theory you just studied applies in data science or you could create real examples to enhance your studying better.
2. Creating multiple algorithms from scratch
It is a great thing that you are creating algorithms from scratch but do you know what’s more important. The way you apply it. Creating algorithms and then working on it can be tiresome and time consuming hence there is already machine learning libraries and cloud based solutions who can do it on your behalf. What you can do is create algorithms for learning and strain your focus more on how you can apply the right algorithms to fix the puzzle.
3. Getting too excited about the future creations
Being excited is great, but in data science that excitement can cost you to miss out on a lot of things. For instance, you may think you could develop a solution which can help to speed the process of lead generation, but did you know that there are also other algorithms which can help the same process to be conducted in a much more eunique and efficient manner than yours? This is exactly why you must be aware of all the approaches and practices being applied.
Ensure that your knowledge isn’t just limited to one thing, explore the data you have, identify and find solutions which can help to frame solutions much better.
4. Creating a technical resume
A resume stands out for you from the crowd. It contain all the information which signifies why you should be chosen from the crowd and if applying for a data scientists work role, all you do is create a resume with high technical terminologies, chances are you may not get the job. Data science is a technical based process but when you list it out on your resume you need to speak more about what you did with the technical terms being used, how were you able to provide a solution.
For Instance if you used machine learning, explain why you used it, for what purpose, what did you achieve from it, what is the problem. Data scientists can be considered a sherpoies who can enhance a business for better, and if you don’t state this matter, don’t be surprised with your job interview outcomes.
5. Not considering communication skills as important
Communication skills matter rin data science and many believe that if they don’t practice or they avoid it is alright. But the fact is it’s not alright. Communication skills are important in data science because when you identify a problem and create a solution you need to explain to a user how it can benefit them. If you are going to use technical jargon to explain the process it could be difficult to even apply the solution.
Hence a data scientist must learn to speak well and convey its solution being offered by them easily and understandably. You can try to practice it with you peers by explaining them the solutions you create or the algorithms you made in an easy and non technical manner, structure out how you can explain your findings in a presentable manner and also you could continuously practice how you speak and break down the technical terms into easily understandable terms for users to understand.
Keeping the above essential information in hand, you need to also keep in mind about the other set of factors which can help make a difference before you can even enter in the data science field.
Essential factors to keep in mind before entering into Data Science
1 . Data science is diverse and there is alot to contribute in it. Data science is applicable to help businesses, research centres and so much more that it is necessary that you know which one you want to get into. Data science is huge and has several factors under it as explained in the above sentences, so it is always important that you keep in mind what matters.
2. It’s alright if you don’t know a few things during your data scientists period. Always be eager to learn and understand the different ways you can use the data available with you for a good purpose. There are many approaches and practices which require your attention and can help you out. Keep exploring your role and the data which you manage to spark better solutions
3. You don’t have to master every data science tools the world has. Not every tool is being used. It depends on the company you work in. Every company would use different tools which you can easily familiarise yourself as you will be working there.
4. It’s always great to have basic knowledge whether it’s your projects, the industry you work in or even the basic tools. Having a brief knowledge can help you understand the workflows better and help carve your thinking towards the creation of an enhanced approach.
5. Develop critical thinking as it can play a huge part in your data science career. You could have all the right tools but when you are not able to identify the purpose of what you are doing or the impact of your actions, you might not be able to deliver much. Hence increase your critical thinking capabilities and work towards finding out the purpose of all your actions
6. Always remember that a good data scientists isn’t just one who has a lot of knowledge or only has knowledge on certain tools, a good data scientist is one who knows how to combine both of these assets to create or identify an exceptional solution.
7. Data science has a lot of scope which is why you need to carve your footsteps towards the right direction. Which type of data scientist you aim to function in mostly?
What are the types of data science jobs?
1. Data analyst
1 . Extracting data from SQL databases
2. Enhancing your skill from Excel
3. Converting data into visualizations (basic)
4. Monitor A/B testing
2. Data Engineer
1 . Work derived more from the analysis basis
2. Requires engineering skills
3. Requires software skills
4. Deals with data infrastructure
3. Machine Learning Engineer
1 . Focuses more on individuals who want to move towards the academic route
2. Has good knowledge of mathematics, statistics, and physics
3. Mostly hired by data-driven companies
4. Data Science Generalist
1 . Conduct analysis
2. Create data visuals
3. Perform production code
What does the future of data scientist looks likes?
Data science will be growing and developing in multiple numbers. Data will be emphasised more and will soon be dependent for any business successes. Even with the rise in technology, new methods will evolve the way a data scientists can retrieve, view and manage data.
‘’Data science is the core of the business because all the operations related to the business depend on the data science from statistics to decision making companies are using data science and its story not ends here. Now data science changes into machine learning and big data.’’
The Bottom Line…
Data is playing a huge role in the business industry. When you have a set of experts such as data scientists next to you working to provide a successful solution, it becomes easier to cater to your prospects. The value being offered to them is much better.
To access such data, many applications have been introduced in the market which could help data scientists to successfully retrieve data, a few examples could be web scraping where data can be extracted from any source via a shorter time frame or a reliable proxy server that can conduct secure web scraping activities as well as allows safer viewing of data online without any chances of any identity to be revealed.
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