SAS Data Science Certification Course - Aspire Techsoft Academy
SAS Introduction?
SAS is Statistical Analysis System (SAS) is an integrated system of software products which is developed by SAS Institute Inc. for data management, advanced analytics, multivariate analysis, business intelligence, criminal investigation, and predictive analytics.
SAS is a set of results for organization-wide business users. It serves as a fourth-generation programming language for performing various functions such as:
SAS is a popular and successful tool used in the field of data analysis and data modeling.? SAS is a statistical analysis system i.e. statistical software suite which is developed by SAS Institute for advanced analytics purposes, data migration, data mining, business intelligence, predictive analytics, investigation, etc.
In SAS you can access SAS tables, data in Excel, and database files in any format. It is used in various fields like data processing, report writing, data mining, statistical analysis, data modeling, software development, and data warehousing.
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Data Science Overview?
Data Science is the study of data to extract meaningful insights for large businesses. Data Science combines the principles and methodologies of mathematics, statistics, specialized programming, advanced analytics, artificial intelligence, and computer engineering to analyze large amounts of data.
Using Data Science many large-scale organizations store necessary information and perform data analytics on it.
SAS is the best tool for Data Science. It is a good platform for Statisticians working in Business Intelligence (BI).
Python and R suited programming languages for Data Scientist professionals working in Natural Language Processing, big data, visualization, etc.
More specifically data related, such as data mining or data engineering, data science involves the entire life cycle of translating raw data into usable information and applying it for productive purposes in different types of applications.
More than 50+ Data Science programs are now being taught in major universities across the country. In today's modern era, Data Scientist is in high demand in business and IT sectors and will continue to grow.
Data Science Life cycle
??Who is a Data Scientist?
Data Scientists are experts in the field of science who apply analytical scientific methods to large volumes of data to gain insights and reduce business risks that help in data-driven strategic planning for organizational decision-making.
A Data Scientist collects and analyzes large amounts of data from a business perspective and applies statistics, machine learning, and data visualization to gain insights that can help drive business decisions.
Data scientists combine the concepts of software engineering and statistics to transform raw data into meaningful information.
?The term Data Scientist was not entirely new a few decades ago, but the need for real data in today's increasingly competitive market has realized the importance of real data, as well as companies surviving in today's growing analytics market, so the demand for data scientists rapidly increasing.
Data Scientist Qualities
·???????? Statistics and machine learning.
·???????? Coding languages such as SAS, R, or Python.
·???????? Databases such as MySQL and Postgres.
·???????? Data visualization and reporting technologies.
·???????? Hadoop and MapReduce.
What’s in a data scientist’s toolbox?
Data Scientist Technical skills
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Why Data Science is important?
Amazon, Flipkart, Datazymes, Dell, Netflix SAS, Genpact, etc. as well as online marketing companies also rely on Data Science to gain customer insights. These businesses continue to use data mining techniques to understand user interests, identify the right customers, and send automation messages to them. Better analyze marketing insights and feedback reviews of products.
Data science makes it possible to solve analytical problems in a business or large company more quickly and objectively. This is the best way to solve the scattered data in the organization. Data science has a wide variety of tools and applications, where business and professional sectors can become more dominant.
Data Scientist works in various companies or industries like healthcare, retail, public sector, banking, government agencies, tech startups, research industries, etc. Data Scientist is in huge demand in today's IT Economy. If you go through the Glassdoor site, you will see that Data Scientist jobs are currently ranked number 1. Data Scientist has high salary packages.
Why Data Science is the most important?
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Data Science Industry Solutions:
Who uses Data Science?
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How Data Science Works: Using Artificial Intelligence & Machine Learning
Data Science helps businesses to gain meaningful insights take appropriate steps to achieve their goals and stay updated in the competitive market.
Data Science helps in understanding the latest trends in the market and improving business operations as well as making more informed decisions.
Data Science helps in understanding the latest trends in the market and improving business operations as well as making more informed decisions.
Data science can predict and predict problems in data processing in an organization and save businesses from losses. By analyzing recurring trends using Data Insights, potential issues in a business can be identified. So planning a data science strategy is important for business growth.
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1) Data Management: ?Data management is essential to unlocking the potential of an organization. It is very important to manage data effectively and collect it for analysis
It is designed to provide the Data Scientist with the necessary skills to acquire real-world data, explore data to find patterns and distributions, and handle large datasets including databases.
A data scientist will learn minimal aspects of Python as needed to acquire datasets. In this, maximum benefit can be obtained with minimum programming using the Python library.
Acquiring data from various online data sources, discovering and understanding aspects of the data, learning various domain-independent and domain-dependent ways to curate data, as well as exploring, managing, and analyzing curated data.
Candidates will also learn how to perform basic analysis of datasets using SQL programming
2) Programming: R Programming is an open-source programming language. Data scientists use R programming for data analytics, data cleaning, data mining, as well as statistical analytics. R programming is used in data visualization projects such as Google Analytics, Twitter, etc.
3) Python: Python is an open-source programming language for data analysis and scientific computing.? Python provides the object-oriented programming approach. Python provides large libraries as well as Artificial Intelligence with a Machine Learning approach. The most useful Python libraries are Numpy, Pandas, Matplotlib, Scipy, Scikit Learn, Seaborne, Requests, etc. It provides functionality with maths, stats, and scientific functions.
4) Data Curation (Big Data and Hadoop):?
Data curation is designed for the Data Scientist, which includes technologies like big data and Hadoop. Data curation plays its role in Data Management Tools and Applications for the SAS Data Scientist.
This includes performing extract, transform, and load (ETL) tasks using SAS Data Integration Studio.? Also, these technologies are used to create data such as data processing such as data labels, data management, data preservation, and enhancement can be done in Data Curation.
Data Curation to share data analytics with organizations and allows data storage accessible to applications. Data curation provides significant benefits of data-driven insights as well as enabling companies to improve financial productivity.
In data curation, data can be processed in activities like contextualizing, Data Citation, Data Arrangement and Description, Cease Data Curation, Code Review,? Curation Log, Data Citation, Data visualization, Data cleaning, Data Identification and validation, Data disk Image, emulation, Documentation, file Format Transformation, File? Audit, and download (rename, update, validation, indexing), Metadata, Quality Assurance, Migration, Secure management, Software Registry,? Transcoding, Data Analytics.
5) SQL Language: SQL Language works in data science to perform operations on stored data such as creating records, retrieving records, updating records, showing tables, filtering, and modifying data. SQL is used as an API for big data platforms. Big data platforms like Hadoop and Spark as well as Oracle, and MySQL are important for performing data analysis operations in SQL.
SQL has some important topics that are regularly used in data science, Aggregation Functions, Group By Clause, String Functions and Operations, Joins, Date and time operations, Output control statements, Various Operators, Query Optimizations, Window Functions, View and Indexing, etc.
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6) Machine Learning: ?
Data is the first source for any company or business, and data has recently increased in demand and importance. For this, data engineers and data scientists need machine learning.
The technology of Machine Learning has made it easier to analyze large amounts of data and risk as well. Machine Learning has changed the way of data engineering in terms of handling and extracting data.
Machine learning uses methods from neural networks, statistics algorithms, operations research, and physics to discover hidden insights in data without programming.
Machine Learning Life cycle
What’s required to create a Machine Learning system
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Why Machine Learning is Important?
1.?????? Machine Learning Is A Vast Subject With Frequent New Developments
2.?????? Machine Learning Is an Area of Academic Growth
3.?????? Machine Learning Is In Demand
4.?????? Machine Learning Is Automating Everything
5.?????? Machine Learning Is Reducing Costs
6.?????? Machine Learning Is A Career With Unlimited Opportunities
7.?????? Machine Learning Helps Business Grow
8.?????? Machine Learning Is Helping Advance Industries
9.?????? Machine Learning Is Transforming Healthcare
10.?? Machine Learning Improves Energy Sector
11.?? Machine learning improves video games
12.?? Machine learning could take over dangerous jobs
13.?? Machine learning helps environmental protections
14.?? Machine learning can improve elder care
15.?? Machine learning can improve banking
16.?? Machine learning can improve cybersecurity
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7) Neural Networks
?A neural network is a set of algorithms that can learn to program, recognize patterns, and make human-like decisions.
Neural Networks are computational systems that act like neurons in the human brain. Using algorithms in Neural Networks, they can identify hidden patterns and correlations in raw data, and improve it over time by classifying it.
Neural Networks a computer system that processes information by relaying information between each unit, and responding to external input.
The term neural network is not new; it dates back to the 40s when Warren McCullough and Walter Pitts working at the University of Chicago came up with the idea of developing artificial intelligence algorithms to mimic human behavior.
AI research accelerated rapidly when Kunihiko Fukushima developed the first true, multi-layer neural network in 1975.
Brain As expected, neural networks are a common area of research and development in both computer science and neuroscience. For example - a facial recognition system that matches your test image to unlock your mobile.
Neural Networks types
1.?????? Convolutional neural networks (CNNs)
2.?????? Recurrent neural networks (RNNs)
3.?????? Feed forward neural networks
4. Auto-encoder neural networks
5.?????? Perceptron
6.?????? Multi-layer Perceptron
7.?????? Convolutional Neural Networks
8.?????? Long Short-Term Memory Networks
9.?????? Generative Adversarial Networks
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?Why are neural networks important?
Credit card and Medicare fraud detection.
Optimization of logistics for transportation networks.
Character and voice recognition, also known as natural language processing.
Medical and disease diagnosis.
Targeted marketing.
Financial predictions for stock prices, currency, options, futures, bankruptcy, and bond ratings.
Robotic control systems.
Electrical load and energy demand forecasting.
Process and quality control.
Chemical compound identification.
Ecosystem evaluation.
Computer vision to interpret raw photos and videos (for example, in medical imaging and robotics and facial recognition).
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8) Deep Learning
Deep Learning in SAS Data Science, you'll learn how to build deep feed-forward, convolutional, recurrent networks, and types of denoising autoencoders.
Deep learning technology allows neural networks to solve problems involving traditional classification, image classification, and sequence-dependent results.
The deep learning course is best for those who are interested in machine learning and deep learning, computer vision, or natural language processing.
Deep learning uses neural networks at multiple levels of processing units as well as technologies on computational systems to learn complex patterns in large amounts of data.
Deep learning is a subset of machine learning that trains computers to perform human-like tasks, such as image manipulation, future insight recognition, etc. Deep learning improves the ability to classify, identify, search, and describe using this data.
Deep Learning with SAS
Deep learning models in SAS
1) Deep feed-forward neural networks (DNN)
2)?Convolutional neural networks (CNN)
3)?Recurrent neural networks (RNN)
If you want to enroll in a deep learning course in data science, you need to be familiar with basic neural network modeling ideas. You can acquire this neural network modeling knowledge by completing the Neural Networks: Essentials or Neural Network Modeling courses. This course addresses SAS Viya, SAS visual data mining, and machine learning software.
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9) Data visualization
In data visualization, you can present and analyze graphical data and empower your organization to make business decisions based on it. You can view and process details on charts and graphs using Data Visualization technology to interactively change and advance your concepts.
Why is Data Visualization so Important in Data Science?
1.?????? Data Visualization Discovers the Trends in Data
2.?????? Data Visualization is Interactive
3.?????? Data Visualization Provides a Perspective on the Data
4.?????? Data Visualization Explains a Data Process
5.?????? Data Visualization Strokes the Imagination
6.?????? Data Visualization Tells a Data Story
7.?????? Data Visualization Puts the Data into the Correct Context
8.?????? Data Visualization is Educational for Users
9.?????? Data Visualization Saves Time
10.?? Data Visualization Presents Data Beautifully
11.?? Identify areas that need attention or improvement.
12.?? Clarify which factors influence customer behavior.
13.?? Help you understand which products to place where.
14.?? Predict sales volumes.
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10) Artificial Intelligence
Artificial Intelligence is a computer science system that is generally capable of acting like human intelligence. There are many approaches to Artificial Intelligence and these are related to creating smart machines. Due to Machine Learning and Deep Learning, radical changes are taking place in the technology sector.
Artificial Intelligence allows smart machines to model and modify human intelligence capabilities.
AI is a growing part of daily life in big industries and businesses like self-driving cars, vehicle recognition identification, digital assistants, robot vacuums, transportation, banking, healthcare, manufacturing, retail, and Alexa, etc. Nowadays, many technologies in various industries are investing in artificial intelligence.
Artificial Intelligence enables machines to learn from experience, adapt to new inputs, and act like minds. Using AI technology, computers collect and process large amounts of data and the numbers in the data can be used to perform specific tasks.
AI technology consists of computer programs that are included in computer science and engineering. It deals with the task of using computer systems to understand human intelligence.
Why Artificial intelligence is the most important?
Benefits of Artificial Intelligence!
·???????? Automation
·???????? Smart Decision Making
·???????? Increase customer experienced
·???????? Research & Data Analysis
·???????? Managing Repetitive Task
·???????? Minimizing Errors
·???????? Increased Business Efficiency
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Artificial Intelligence & Machine Learning Applications
Speech recognition
Language Translation
Product Recommendation
Customer service
Computer vision
Search Recommendation Engines
Automated stock trading
Maps and Navigation
Face Detection
Text Editors or Autocorrect
Email spam and Malware detection
Stock Market Trading
Medical Science
Healthcare
Banking & Finance?????????
Gaming and entertainment
Air transport
Chatbots
Social Media
E-Payments
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Computer Vision
Computer Vision is a field in Artificial Intelligence that trains computers to gain insight and understanding through visualization.
Computer vision uses deep learning to recognize and classify videos and images. Like, as face recognition, analysis by scanning, etc.
This technology has been applied in areas like self-car driving, automation, facial recognition, medical diagnosis, and agricultural intelligence.
Types of Computer vision:
·???????? Image segmentation
·???????? Object detection
·???????? Facial recognition
·???????? Edge detection
·???????? Pattern detection
·???????? Image classification
·???????? Feature matching
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Computer Vision applications
Parking occupancy detection
Traffic flow analysis
Road condition monitoring
领英推荐
X-Ray analysis
CT and MRI
Cancer detection
Blood loss measurement
Digital pathology
Inspection
Reading text and barcodes
Product assembly
PPE Detection
Agriculture
Crop and yield monitoring
Automatic weeding
Insect Detection
Video analytics
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11) Natural Language Processing
Natural Language Processing is an area of artificial intelligence that specifically provides Artificial Intelligence computers with the ability to understand spoken words. Natural Language Processing (NLP) helps computers understand and interpret human language. Also, these technologies enable computers to process text or voice data on human language and 'understand' the full meaning of speaker intentions and emotions.
Natural Language Processing Capabilities
·???????? Content categorization
·???????? Topic discovery and modeling
·???????? Corpus Analysis
·???????? Contextual extraction
·???????? Sentiment analysis
·???????? Speech-to-text and text-to-speech conversion
·???????? Document summarization
·???????? Machine Translation
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SAS Certified Data Scientist Complete Career Path
SAS Certified Data Science Courses
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Qualifications & Eligibility Required for the SAS Data Science Certification Course
Computer Science, Math & Statistics, Management Information Systems, Economics, Engineering, and Hard Sciences.
Educational Degrees -
MTech /MCA Data Science is a BCA / BE / BTech / BSC or MSC in computer Science,
Maths &Statistics degree,
PGDM in Research and Business analytics,
MPhil in Machine Learning,
MSC in Engineering,
MSc Health Data Analytics and Machine Learning,
?MSc Artificial Intelligence, MSc Applied Computing.
Software Developers, Data Analysts, Business Analysts, and System/Database Administrators can take the Data Science Certification Course in Pune
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Most Important Concepts to Learn in SAS Data Science
Statistics & Mathematics
Data processing
Data visualization
Data storage
Programming languages (Python, R, and more)
Big Data & Hadoop
SQL Server
Neural Networks
Deep Learning
AIMS
Predictive Modeling
Data Mining
Data Warehousing
Business Intelligence
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The Below details are the complete career path for the SAS Certified Data Scientist
SAS Data Science Certification Includes courses
1.?????? Data Curation Professional
This certification course is designed for those who want to prepare data using SAS data management tools and applications for statistical analysis and Data Curation using third-party tools.
Designed by SAS Experts, this course covers SAS topics for Data Curation Techniques including Hadoop with Big Data. Before Enrolling in this course, you need to be familiar with SAS Programming skills, Data Manipulation skills, and SQL Language.
This allows you to complete this experience with SAS Programming 1, SAS Programming 2, Data Manipulation Techniques, and SQL courses.
SAS Data Curation Professional Course Includes 4 Courses
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Exam & Credentials
Use Exam ID A00-223; required when registering with Pearson VUE.
The exam takes 110 minutes to complete.
67% correct marks are required to pass this exam.
It consists of 65-72 multiple-choice, short-answer, and interactive questions.
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2.?????? Advanced Analytics Professional
This certification course will be best for those who want to expand their analytical skills in their operations by learning analytical modeling, machine learning, experimentation, forecasting, and optimization.
If you want to learn the SAS Advanced Analytics Professional certification course completely then you must have experience in programming skills. It is necessary to have knowledge of statistics.
If you just want to get started, you can enroll in online eLearning courses like SAS Programming 1, SAS Programming 2: Data Manipulation Techniques, Statistics 1: ANOVA, Regression, and Logistic Regression. And SAS Programming for R Users etc.
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The SAS Certified Advanced Analytics Professional course includes 3 courses-
A.????? Predictive Modeling
In this course, you will learn how to perform analysis using SAS Enterprise Miner for both pattern discovery and predictive modeling (decision trees, regression, and neural network models). This course is suitable for SAS Enterprise Miner 5.3
This course will enable you to do SAS Predictive Modeling using SAS Enterprise Miner 14 Certification Preparation.
Use this exam ID to register: A00-255
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B.????? Advanced Predictive Modeling
In this certification course, you will learn how to optimize and develop predictive models using SAS Enterprise Miner.
In this course, you will learn the following SAS data preparation tools SAS Enterprise Miner, SAS/STAT, SAS Visual Analytics, and SAS LASR Analytic Server.
SAS Certified Specialist: Advanced Predictive Modeling
Use this exam ID to register: A00-225
It includes the following courses.
Neural Network Modeling
A neural network model creates a multilayer neural network that passes from one layer to another to map input into predicted values.
This course will be best for those who want to learn Data Analyst, Qualitative Expert, and other SAS Enterprise Miners advanced skills.
This course helps you to understand and apply the algorithm of Neural Networks.
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Predictive Modeling Using Logistic Regression
This course addresses SAS/STAT software. This course covers how to perform Predictive Modeling using Logistics processes.
This course covers selecting variables and interactions, evaluating models, recoding categorical variables based on a smooth weight of evidence, treating missing values, and applying efficiency techniques to large data sets.
This course will be best for those who are predictive modelers, data analysts, and statisticians who can create predictive models, especially in sectors such as banking, financial services, direct marketing, insurance, and telecommunications industries.
Data Mining Techniques: Predictive Analysis on Big Data
In this course, you will learn applications and techniques for Big Data and Modeling. It introduces basic and advanced modeling strategies, such as group-wise procedures for linear models, random forests, generalized linear models, and mixture distribution models.
This course will be best for those who are business analysts, data analysts, marketing analysts, marketing managers, data scientists, data engineers, financial analysts, data miners, statisticians, and others who work in related fields.
This course addresses SAS Enterprise Miner, SAS In-Memory Statistics software, and SAS Visual Statistics.
Today, large businesses as well as large industries are realizing the value of predictive analytics, which gives the organization a competitive advantage.
SAS analytics tools enable a complete analytics-lifecycle process that simplifies organizations from data to decisions at scale, with highly reliable methods.
Data Mining
Big Data Analysis
The biggest players in Big Data Technologies
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Cloud Computing
Data Management
Data Mining
Data Storage
Hadoop
In-Memory Analytics
Machine Learning
Predictive Analytics
Text Mining
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?Why is Big Data Analytics important?
Using SAS to Put Open-Source Models into Production
Reducing cost
Making faster, better decisions
Developing and marketing new products and services
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C.????? Text Analytics, Time Series, Experimentation and Optimization
Candidates getting the credentials of this course must pass all 5 SAS exams and get the credentials of SAS Big Data Professionals and SAS Advanced Analytics.
This certification course will be best for those who want to analyze Big Data with statistical analysis and predictive modeling techniques.
Candidates who will be successful in this will get experience in following Analytical tools.
SAS Text Analytics, SAS/ETS, SAS/OR
Required Certification:
SAS Certified Specialist: Text Analytics, Time Series, Experimentation and Optimization
Use exam ID A00-226; required when registering with Pearson VUE
This exam is based on SAS 9.4
There are 4 courses included in Text Analytics, Time Series, Experimentation, and Optimization which are as follows.
??????? I.??????????? Text Analytics Using SAS Text Miner
????? II.??????????? Time Series Modeling Essentials
??? III.??????????? Experimentation in Data Science
??? IV.??????????? Optimization Concepts for Data Science
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??????? I.??????????? Text Analytics Using SAS Text Miner
In this course, you will learn the concepts of SAS Text Miner, document sharing, as well as classifying documents into predefined categories, predictive modeling, data structuring data synchronization, etc.
This course will be best suited for statisticians, business analysts, and market researchers who incorporate open-text data into their analyses. Also, Data Mining Candidates who collect and manage Big Data documents want to know about Text Mining.
In this, you will learn how to convert any document stored in standard format like Word format, PDF, or PPT, to HTML or text format. Documents from various sources in SAS tables will be easier to read.
This course addresses the SAS text miner software.
This course uses SAS Text Miner 15.1 and SAS Enterprise Miner 15.1.
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??????? I.??????????? Time Series Modeling Essentials
This course is on the methodology of modeling time series data. You will learn three models to analyze univariate time series with exponential smoothing, autoregressive integrated moving average with exogenous variables (ARIMAX), and unknown components (UCM).
This course will be best for analysts with a quantitative background as well as non-statistical analysts and domain experts who want to enhance their time series modeling proficiency.
This course addresses SAS/ETS and SAS Studio software.
Before enrolling in this course, you must be familiar with statistical concepts. This requires studying Statistics 1: ANOVA, Regression, and Logistic Regression courses.
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??????? I.??????????? Experimentation in Data Science
In this course, you will learn how to design and execute projects in Data Science Like, feature engineering or machine learning. It addresses the SAS Enterprise Miner software.
This course will be best for those who are data scientists, business analysts, market researchers, statisticians, and others who want to use experiments and incremental response models in business.
This course will be helpful for SAS Certified Specialists: Text Analysis, Time Series, Experiments, and Optimization. If you are an experienced data scientist or just starting your career, you can enroll in this course.
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??????? I.??????????? Optimization Concepts for Data Science
This course is based on nonlinear optimization. In this, you will learn how to debug optimization and how to create index sets using arrays and OPTMODEL process optimization.
This course will be best for those who want to develop the optimization foundation needed to work as a data scientist, especially those with a strong professional background in applied statistics & and mathematics.
Before enrolling in this certification course, you must have completed all courses for the SAS Certified Big Data Professional program or passed the Certification exam in Big Data. Also, you should be able to perform data manipulation using SAS Basics tools. For this you can gain this course-specific knowledge in data manipulation by completing the SAS Programming 1: Essentials course.
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3.?????? Artificial Intelligence & Machine Learning Professional
Module 1: Machine Learning Using SAS Viya
In this course you will learn machine learning models, SAS Viya Data Mining, SAS Visual Text Analysis in SAS Viya, and Deep Learning Using SAS software techniques. A series of demonstrations and methods are used to reinforce the analytical approach to solving all concepts and business problems.
It uses the Pipeline Flow interface in Model Studio, SAS Viya, which enables you to create, develop, and compare advanced analysis models across the enterprise.
This course will be best for those who are Business analysts, Data Analysts, Marketing Analyst, Marketing Managers, Data Scientists, Data engineers, Financial Analyst, Data Miner, statisticians, mathematicians, and others who work in related fields.
Required Certification:
SAS Certified Specialist: Machine Learning Using SAS Viya 3.5
Use exam ID A00-402; required when registering with Pearson VUE.
This exam is based on SAS Viya 3.5.
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Module 2: Natural Language Processing and Computer Vision
For data scientists, text and image analysts, AI specialists, and others who analyze text and image data to recognize patterns.
Required Certification:
SAS Certified Specialist: Natural Language Processing and Computer Vision Using SAS Viya 3.5
Use exam ID A00-405; required when registering with Pearson VUE.
This exam is based on SAS Viya 3.5.
Module 3: Forecasting and Optimization
In this course, you will learn these concepts. Data Visualization, Pipeline Modeling, Hierarchical Forecasting, Post-Forecasting Functionality, Optimization etc.
This course will be best for those who are data scientists, forecasters, analysts, and economists.
Required Certification:
SAS Certified Specialist: Forecasting and Optimization Using SAS Viya 3.5
Use exam ID A00-403; required when registering with Pearson VUE.
This exam is based on SAS Viya 3.5.
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Data Science Future Job Opportunities
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Data Science Job statistics?
?Future Scope of Data Science
Data Science is currently the fastest-growing thing in the world of Analytics. Many big companies are looking for Data Scientists with a very good salary range. The future world is mostly dependent on data and with it, the demand for Data Scientist Experts will increase.
The future of Data Science jobs will be bright and prosperous. Businesses are also moving towards investing more in improving their data infrastructure and promoting data science implementation.
Data Scientist Salary in India
Career opportunities in Data Science are increasing rapidly over the last few years. Data science has become important in large companies to capture data and gain insights from it. Companies are paying huge salaries to those who have skills for the posts of Engineer, Data Scientist Data Analyst, etc.
Roles and responsibilities of SAS Data Scientist?
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Conclusion:
Data Science has become a promising career in today's world. Big companies and businesses have undergone a radical change due to Data Science. Data science roles require familiarity with a specific tool along with the fundamentals of data science, as programming skills are essential.
Data Science 3 Popular Programming Languages are used. R Programming, Python, SAS. SAS is a nearly 50-year-old data science tool that meets industry demands.
If you are interested in a career as a Data Scientist as well as becoming a data scientist then we have the right guide for you. A data science career will give you insight into the trending technologies and skills that are running today.
If you want to learn data science then you can Enroll with Aspire Techsoft's Best SAS Data Science Certification Course in Pune get guidance from our Data Science course Training Advisor and start your Data Science Career.
If you want to acquire the right goals and skills in Data Science then this is the right time. Also, you can consider a Data Science Course in Pune based on your experience level and skills and get admission to a data science career. More Data Science Career opportunities will be available in the future. Go ahead and make a good career move toward Data Science!
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