Data Science Certification Training in Istanbul

Introduction:

Data science in Istanbul can be defined as a combination of mathematics, business acumen, tools, algorithms, and machine learning techniques that aid in the discovery of hidden insights or patterns in raw data that can be used to make important business decisions.Statistics, tools, and business knowledge are all combined in Data Science. As a result, a Data Scientist must be well-versed in and comprehend these concepts.

In data science, both structured and unstructured data are dealt with. Predictive analytics is also used in the algorithms. Thus, data science is concerned with the present and future. That is, identifying trends based on historical data that can be used to make current decisions, as well as identifying patterns that can be modelled and used to make predictions about how things might look in the future.

Tools Involved in Data Science:

Extensive knowledge of R:

• R is used for data analysis, as a programming language, as a statistical analysis environment, and for data visualisation.
• Python programming: Python is widely used to implement mathematical models and concepts because it has a large number of libraries/packages for building and deploying models.

Microsoft Excel:

• Microsoft Excel is considered a must-have for all data entry jobs.
• It is extremely useful in data analysis, extracting formulae, equations, and diagrams from a jumbled mess of data.

Platform for Hadoop:

• It is a distributed processing framework that is open source.
• It is used to manage the processing and storage of large amounts of data.

SQL database/programming:

• It is primarily used for dataset preparation and extraction.
• It can also be used to solve problems such as graph and network analysis, search behaviour, fraud detection, and so on.

Technology:

• Because there is so much unstructured data available, it is also necessary to understand how to access it.
• This can be accomplished in a variety of ways, including through APIs and web servers.

Data Science Components in Istanbul:

Data Science is divided into three parts:

• Machine Learning entails algorithms and mathematical models, which are primarily used to teach machines to learn and prepare them to adapt to everyday advancements.
• Time series forecasting, for example, is widely used in trading and financial systems these days.
• In this case, the machine can predict the outcomes for the next few months or years based on historical data patterns.
• This is an example of machine learning in action.

Big Data & Analytics:

• Every day, humans generate enormous amounts of data in the form of clicks, orders, videos, images, comments, articles, RSS feeds, and so on.
• These data are typically unstructured and are referred to as Big Data.
• Big Data tools and techniques primarily aid in the conversion of unstructured data into structured data.
• Assume someone wants to keep track of the prices of various products on e-commerce sites.
• Using Web APIs and RSS Feeds, he/she can access data for the same products from various websites.
• Then organise them into a structured format.

Intelligence in Business:

• Every business has and generates an excessive amount of data on a daily basis.
• This data, when carefully analysed and presented in visual reports with graphs, can bring good decision making to life.
• This can assist management in making the best decision possible after carefully delving into the patterns and details revealed by the reports.

Skills Covered in Data Science:

• Learn everything there is to know about data structure and data manipulation.
• Understand and apply linear and nonlinear regression models, as well as classification techniques, for data analysis.
• Learn about supervised and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipeline.
• Use the SciPy package and its subpackages such as Integrate, Optimize, Statistics, IO, and Weave to perform scientific and technical computing.
• Learn about mathematical computing with the NumPy and Scikit-Learn packages.
• Learn about the various components of the Hadoop ecosystem.
• Learn how to work with HBase, its architecture, and data storage, as well as the differences between HBase and RDBMS, and how to partition data using Hive and Impala.
• Learn about MapReduce and its characteristics, as well as how to ingest data with Sqoop and Flume.
• Learn about recommendation engines and time series modelling, as well as the principles, algorithms, and applications of machine learning.
• Learn how to use Tableau to analyse data and create interactive dashboards.

Roles and Responsibilities:

A data scientist is a person who excels at statistics. Let's take a look at some responsibilities:

• Data should be gathered from a variety of sources, all of which must be reliable. To make the process easier, the data collection process can be automated.
• Data cleaning is a critical step in any data analysis project because it consumes the majority of the data scientist's time. Missing data should be filled in, and unnecessary fields should be skipped.
• Data analysis must be performed correctly in order to identify various trends and patterns in the data.
• Machine learning should be used to build models in order to thoroughly understand and analyse the data.
• Training and test datasets must be correctly identified and separated from understanding the impact of data.
• To understand the data pattern, various models should be combined and thoroughly studied.
• To make good business decisions, data must be properly organised and understood by everyone on the team.
• They should be good listeners to the team and observers of various data findings.
• Data scientists must interpret data carefully because incorrect interpretations can have disastrous consequences for the company.
• Data scientists should convert collected data, whether structured or unstructured, into a meaningful format so that even an employee from a different department can understand the data.
• Being a good mathematician aids data scientists in easily segregating data, identifying trends, and identifying correlations.
• They should be kept up to date on all of the latest data trends in the industry for his benefit.
• The domain knowledge in which he works is important because it aids in the understanding of the data.
• This is done to avoid unnecessary data and to consider only necessary data.
• Data scientists should work with other departments to collect data from their fields and be well-versed in their work.
• After data analysis, data scientists' insights should be relevant to the domain, and the change should be reflected in the company's profit.
• Analysis of past data aids in understanding the behaviour of data, and forecasting the future aids in planning the future accordingly.

Career path:

1. Data Analyst:

• A data scientist is the most sought-after data science career path among professionals.
• If you want to become a data scientist, you will be responsible for data collection, analysis, and actionable insights based on large amounts of raw data.
• If you want to pursue a career in data science, you should be interested in technology and have a strong background in mathematics, statistics, and programming skills.

2. Data Scientist:

• A data analyst gathers information from various sources and analyses it to gain actionable insights.
• They are in charge of transforming and manipulating large data sets in order to match the companies' desired analysis.
• A data analyst recommends various methods and techniques that can assist a company in improving the quality of its data systems.

3. Data Scientist:

• This is another well-known data science career path that attracts a large number of professionals.
• A data engineer is in charge of building and maintaining data pipelines that help data scientists access information.
• They are also in charge of developing new APIs to support the increased data complexity.
• They collaborate closely with front-end and back-end developers, as well as product managers and analysts.

4. Architect of Data:

• They are in charge of creating a blueprint for all data management systems.
• Each company's database must be built and maintained by identifying all potential structural and installation solutions.
• A data architect is in charge of this task.
• It is their responsibility to ensure that their company's data solutions are built for performance and to design analytics for multiple platforms.

5. Analyst for Business Intelligence:

• They convert data into insights that drive business value.
• This data science career path is best suited for individuals with strong critical thinking and problem-solving skills.
• Data analytics, data visualisation, and data modelling techniques are used by BI analysts.
• BI analysts can identify trends that can assist managers in making business decisions that will improve the overall performance of the organisation.

6. Statisticians:

• A statistician has a keen sense for spotting patterns in data.
• They are experts in mathematics and statistics who use statistical methods to solve real-world problems.
• They are in charge of gathering data, identifying trends and connections in data, and communicating the findings to stakeholders.

Advantages of Data Science:

1. Data Science Training improves candidates' career prospects:

• Data Science training enables candidates to advance in their careers.
• We all know that the demand for data science professionals is increasing in almost all major industries.
• A data scientist is required not only in the leading sectors, but also in the world's most prestigious locations.
• Most of the world's most prominent business locations offer Data Science jobs.
• The candidate can obtain a job in Data Science by acquiring data science proficiency, skills, and technology through data science training.

2. With the assistance of Data Science Training, you can obtain certifications in the following demanding Big Data technologies:

• Data Science training prepares you for the growing demand for Big Data skills and technology.
• Data Science training equips professionals with data management technologies such as Hadoop, Flume, and machine learning, among others.
• If a candidate possesses knowledge and proficiency in these critical data skills, they will have an advantage in advancing and competing in their career.
• When a candidate becomes an expert in Big Data and Data Science technologies, it is simple for them to obtain the top Data Science job Titles with a high salary range.

3. Data Science Training prepares you for the highest-paying Data Science job title with Big Data skills and expertise:

• There are a variety of job titles created by Big Data and Data Science technologies that pay well in comparison to other IT jobs.
• These two technologies are not limited to the field of information technology; they are used in all major industries today.
• As a result, a certified Data Science professional has virtually limitless job opportunities in every field.
• So, let's take a look at some of the job titles for Data Scientist.

4. With the help of Data science training, a candidate can be hired by one of the following Fortune 500 companies:

• Many businesses are now looking for Data Science professionals. Facebook, PayPal, eBay, Google, Amazon, Microsoft, and Apple are just a few of the top companies.
• With the help of Data Science training, a candidate can gain expertise and skills that they can include on their resume as a label to gain entry into these top Fortune companies.
• If a candidate wishes to be hired by one of these companies, he or she should obtain the necessary Data Science Training.

5. Data Science Training qualifies you to work in new positions:

• The majority of employers are implementing Big Data and analytics technologies, which will more than double by 2019.
• As a result, employment of big data and data science professionals will reach an all-time high by 2019.
• Data science training equips you with the necessary knowledge, skills, technology, and expertise to launch a successful career in a field with a high number of job openings.
• Big Data and Data Science are vast fields that will not go away in the future.
• As a result, a career in data science is expected to be long-term.

6. Data Science training is delivered by industry experts rather than PhD scientists:

• If you decide to pursue Data Science training, is it from a PhD holder who has no experience on a real-world professional project but has published their work, or from an instructor who has expertise in a real-world professional project? It is advantageous to have an instructor who has valuable industry experience in the relevant field.
• It may be difficult to locate these individuals, but it is advantageous to study under them.
• They can teach you in the context of job experience in a real-life situation, which is what the majority of candidates require the most.

7. Individual Focus:

• In a college, however, it is difficult for students to receive personalised attention.
• However, in Data Science programmes, each student can receive individualised attention based on their needs.
• Every person is unique and will have their own interpretation of the projects.
• When the batch size is less than 30, they can get the proper attention from the expert, which is the most significant benefit of Data Science training.

Payscale:

1. The Data Science in Istanbul position is one of the highest-paying jobs of the twenty-first century.

2. The typical salary is $100,000. Starting salaries for those with advanced degrees in data science range from $5000 to $90,000 per year.

3. The salary of a data scientist is determined by experience, education, and industry.

4. The higher the salary, the greater the experience and education.

5. In India, the average salary is 100,000 rupees.

6. It is determined by the location. Data Science jobs aren't going away any time soon.

7. A data science job is one of the most desirable jobs of the twenty-first century.

8. A data scientist must be knowledgeable in a variety of fields in order to excel in his field.