Data Science Certification Training in Doha

Introduction:

This introductory course provides a high-level overview of key Data Science topics. A fundamental understanding of Data Science from both a business and technological standpoint is provided, as well as an overview of common benefits, challenges, and adoption issues. This course will teach you the fundamentals of data science as well as how to use Python, a powerful open source tool. You will learn about exploratory data analysis, statistics fundamentals, hypothesis testing, regression and classification modelling techniques, and machine learning. The course's final project and interview preparation will prepare you for the workforce.

Data Science in Doha are in high demand, having been named the sexiest career of the twenty-first century by none other than the Harvard Business Review. Data Sceience have been shown to earn up to 36% more in base pay than other predictive analytics professionals. According to Glassdoor, the national average salary for a Data Sceiencein the United States is $1,39,840.KnowledgeHut's Data Science Foundation course will help both new and experienced professionals gain a thorough understanding of the subject and advance their careers.

Career Path of Data Science:

Sceiencein Machine Learning:

• Typical Job Requirements: Investigate novel data approaches and algorithms for use in adaptive systems, such as supervised, unsupervised, and deep learning techniques.
• Machine learning Sceience are frequently referred to as Research Sceience or Research Engineers.

Architect of Applications:

• Typical Job Requirements: Monitor the behaviour of business applications and how they interact with one another and with users.
• Applications architects are also responsible for designing application architecture, which includes building components such as user interface and infrastructure.

Architect, Enterprise:

• Typical Job Requirements: An enterprise architect is in charge of aligning an organization's strategy with the technology required to achieve its goals.
• In order to do so, they must have a thorough understanding of the business and its technological requirements in order to design the systems architecture required to meet those requirements.

Data Engineer:

• Typical Job Requirements: Ensure that data solutions are designed for performance and that analytics applications are designed for multiple platforms.
• In addition to developing new database systems, data architects frequently seek ways to improve the performance and functionality of existing systems, as well as work to provide database administrators with access.

Infrastructure Designer:

• Typical Job Requirements: Ensure that all business systems are operating at peak efficiency and that you can support the development of new technologies and system requirements.
• Cloud Infrastructure Architect is a similar job title that oversees a company's cloud computing strategy.

Engineer, Data:

• Typical Job Requirements: Perform batch or real-time processing on collected and stored data.
• Data engineers are also in charge of creating and maintaining data pipelines within an organisation, which creates a robust and interconnected data ecosystem and makes information available to data Sceience.

Developer of Business Intelligence (BI):

• Business intelligence developers design and develop strategies to help business users quickly find the information they need to make better business decisions.
• They are extremely data-savvy and use BI tools or develop custom BI analytic applications to help end-users understand their systems.

The following are the roles and responsibilities of a data science:

• Data mining is the process of extracting useful data from valuable data sources.
• Selecting features, creating and optimising classifiers with machine learning tools.
• Preprocessing both structured and unstructured data.
• Improving data collection procedures to include all pertinent information for the development of analytic systems.
• Data processing, cleansing, and validation to ensure the integrity of data for analysis.
• Analyzing large amounts of data to discover patterns and solutions.
• Prediction systems and machine learning algorithms are being developed.
• Results must be presented in a clear and concise manner.
• Provide solutions and strategies for dealing with business challenges.
• Work with the business and IT teams to achieve your goals.

To become a data Sceience you must have the following skills:

• Programming abilities – knowledge of statistical programming languages such as R and Python, as well as database query languages such as SQL, Hive, and Pig, is preferred.
• Knowledge of Scala, Java, or C++ is advantageous.
• Statistics – Excellent applied statistical skills, such as knowledge of statistical tests, distributions, regression, maximum likelihood estimators, and so on.
• Statistics knowledge is essential for data-driven businesses.
• Machine Learning – thorough understanding of machine learning methods such as k-Nearest Neighbors, Naive Bayes, SVM, and Decision Forests.
• Strong Math Skills (Multivariable Calculus and Linear Algebra) - Understanding the fundamentals of Multivariable Calculus and Linear Algebra is critical because they serve as the foundation for many predictive performance or algorithm optimization techniques.
• Data Wrangling – The ability to deal with flaws in data is an important aspect of a data Sceiences job description.
• Experience with data visualisation tools such as matplotlib, ggplot, d3.js, and Tableau, which aid in visually encoding data.
• Excellent Communication Skills – It is critical to be able to explain findings to both technical and non-technical audiences.
• Strong background in software engineering.
• Hands-on experience with data science tools is required.
• Ability to solve problems.
• Analytical mind with excellent business acumen.
• A bachelor's degree in computer science, engineering, or a related field is preferred.
• Experience as a Data Analyst or Data Sceienceis required.

Advantages of Data Science Training:

1.Improves commercial predictability:

• When a company invests in data structuring, it can use what is known as predictive analysis.
• With the assistance of a data Sceience it is possible to use technologies such as Machine Learning and Artificial Intelligence to work with the company's data and, as a result, perform more precise analyses of what is to come.
• As a result, you increase business predictability and can make decisions today that will positively impact your company's future.

2.Provides near-real-time intelligence:

• The data Sceiencecan collaborate with RPA professionals to identify their company's various data sources and create automated dashboards that search all of this data in real-time and in an integrated manner.
• This intelligence is critical for your company's managers to make more accurate and timely decisions.

3.Preferential treatment for marketing and sales:

• Data-driven Nowadays, marketing is a generic term. The reason is simple: we can only offer solutions, communications, and products that are truly in line with customer expectations if we have data.
• As we've seen, data Sceience can combine data from various sources to provide their teams with even more accurate insights.
• Can you imagine having access to the entire customer journey map, taking into account all of the interactions your customers had with your brand? Data Science makes this possible.

4. Enhances data security:

• The work done in the area of data security is one of the advantages of Data Science.
• In that sense, there is an infinite number of possibilities.
• Data Sceience, for example, work on fraud prevention systems to keep your company's customers safe.
• He can, on the other hand, study recurring patterns of behaviour in a company's systems to identify potential architectural flaws.

5. Aids in the interpretation of complex data:

• When we want to cross different data sets to better understand the business and the market, Data Science is a great solution.
• We can mix data from "physical" and "virtual" sources for better visualisation depending on the tools we use to collect data.

6. Makes the decision-making process easier:

• Of course, based on what we've shown so far, you can already imagine that one of the benefits of Data Science is improved decision-making.
• This is because we can create tools to view data in real-time, allowing business managers to be more agile.
• This is accomplished through the use of dashboards as well as projections made possible by the data Sceiences treatment of data.

The following are the top 15 data science certifications:

• Analytics Professional Certification (CAP).
• Data Analyst Cloudera Certified Associate (CCA).
• Data Engineer, Cloudera Certified Professional (CCP).
• Senior Data Sceienceat the Data Science Council of America (DASCA) (SDS).
• Principle Data Sceienceat the Data Science Council of America (DASCA) (PDS).
• Data Science Track at Dell EMC (EMCDS).
• Certification as a Google Professional Data Engineer.
• Professional Certificate in Data Science from IBM.
• Azure AI Fundamentals is a Microsoft Certified Professional programme.
• Azure Data SceienceAssociate is a Microsoft Certified Professional.
• Certified Data SceiencePositions Available (Open CDS).
• SAS AI & Machine Learning Professional certification.
• SAS Big Data Professional certification.
• SAS Data SceienceCertification.
• Tensorflow Developer Certification.

Industry Trends:

1. Deepfake Explosion:

• Deepfakes employ artificial intelligence to manipulate or create content in order to impersonate another person.
• Often, this is an image or video of one person that has been altered to look like someone else.

2. Additional Python Applications:

• Python has a plethora of free data science libraries, such as Pandas, and machine learning libraries, such as Scikit-learn.
• It is even capable of being used to create blockchain applications.
• When you combine this with a user-friendly learning curve for beginners, you have a recipe for success.
• According to the analyst firm RedMonk, Python is now the third most popular language in general.
• And the popularity growth trend indicates that it is on track to become the number one neologism in the world.

3. Increased Demand for Full-Stack AI Solutions:

• Dataiku, an enterprise AI company, is now valued at $1.4 billion (according to TechCrunch), following Google's purchase of a stake in the company in December 2019.
• The AI startup assists enterprise customers in cleaning large data sets and developing machine learning models.
• Companies such as General Electric and Unilever can gain valuable, deep learning insights from their massive amounts of data in this manner.
• Additionally, important data management tasks should be automated.
• Previously, businesses had to seek expertise in all aspects of the process and piece it together on their own.

4. Businesses Hire More Data Analysts:

• It is also becoming more common for data professionals to be involved in the output process.
• Because AI-generated results are not always reliable or accurate, machine learning companies frequently employ humans to clean up the final data.
• And write up their findings in a way that non-tech stakeholders can understand.
• Data science and machine learning methods in the 2020s will be less artificial and automated than previously anticipated.
• Artificial intelligence with human-in-the-loop and augmented intelligence will most likely become a big trend in data science.

5. Kaggle Adds Data Sceience:

• Many aspiring data Sceience now begin their machine learning journey with Kaggle.
• And live-stream the progress of their machine learning projects.
• Users can even share data sets and compete in data science competitions using neural networks.
• Alternatively, collaborate with other data Sceience to create models in Kaggle's web-based data science workbench.

Payscale of Data Science in Doha:

1. The average salary for a data science in Doha is $698,412.

2. With less than a year of experience, an entry-level data Science can earn around $500,000 per year.

3. Early-career data Sceience with 1 to 4 years of experience earn around 610,811 per year.