Instructor Led Live Online
Self Learning + Live Mentoring
Customize Your Training
The entire training includes real-world projects and highly valuable case studies.
IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.
MODULE 1: DATA SCIENCE COURSE INTRODUCTION
MODULE 2: DATA SCIENCE ESSENTIALS
MODULE 3: DATA SCIENCE DEMO
MODULE 4: ANALYTICS CLASSIFICATION
MODULE 5: DATA SCIENCE AND RELATED FIELDS
MODULE 6: DATA SCIENCE ROLES & WORKFLOW
MODULE 7: MACHINE LEARNING INTRODUCTION
MODULE 8: DATA SCIENCE INDUSTRY APPLICATIONS
MODULE 1: PYTHON BASICS
MODULE 2: PYTHON CONTROL STATEMENTS
MODULE 3: PYTHON DATA STRUCTURES
MODULE 4: PYTHON FUNCTIONS
MODULE 5: PYTHON NUMPY PACKAGE
MODULE 6: PYTHON PANDASPACKAGE
MODULE 1: OVERVIEW OF STATISTICS
MODULE 2: HARNESSING DATA
MODULE 3: EXPLORATORY DATA ANALYSIS
MODULE 4: HYPOTHESIS TESTING
MODULE 5: CORRELATION AND REGRESSION
MODULE 1: MACHINE LEARNING INTRODUCTION
MODULE 2: PYTHON NUMPY & PANDAS PACKAGE
MODULE 3: VISUALIZATION WITH PYTHON
MODULE 4: ML ALGO: LINEAR REGRESSION
MODULE 5: ML ALGO: KNN
MODULE 6: ML ALGO: LOGISTIC REGRESSION
MODULE 7: PRINCIPLE COMPONENT ANALYSIS (PCA)
MODULE 8: ML ALGO: K MEANS CLUSTERING
MODULE 1: MACHINE LEARNING INTRODUCTION
MODULE 2: ML ALGO: LINEAR REGRESSSION
MODULE 3: ML ALGO: LOGISTIC REGRESSION
MODULE 4: ML ALGO: KNN
MODULE 5: ML ALGO: K MEANS CLUSTERING
MODULE 6: PRINCIPLE COMPONENT ANALYSIS (PCA)
MODULE 7: ML ALGO: DECISION TREE
MODULE 8 : ML ALGO: NAÏVE BAYES
MODULE 9: GRADIENT BOOSTING, XGBOOST
MODULE 10: ML ALGO: SUPPORT VECTOR MACHINE (SVM)
MODULE 11: ARTIFICIAL NEURAL NETWORK (ANN)
MODULE 12: ADVANCED ML CONCEPTS
MODULE 1: TIME SERIES FORECASTING - ARIMA
MODULE 2: FEATURE ENGINEERING
MODULE 3: SENTIMENT ANALYSIS
MODULE 4: REGULAR EXPRESSIONS WITH PYTHON
MODULE 5: ML MODEL DEPLOYMENT WITH FLASK
MODULE 6: ADVANCED DATA ANALYSIS WITH MS EXCEL
MODULE 7: AWS CLOUD FOR DATA SCIENCE
MODULE 8: AZURE FOR DATA SCIENCE
MODULE 1: DATABASE INTRODUCTION
MODULE 2: SQL BASICS
MODULE 3: DATA TYPES AND CONSTRAINTS
MODULE 4: DATABASES AND TABLES (MySQL)
MODULE 5: SQL JOINS
MODULE 6: SQL COMMANDS AND CLAUSES
MODULE 7 : DOCUMENT DB/NO-SQL DB
MODULE 1: GIT INTRODUCTION
MODULE 2: GIT REPOSITORY and GitHub
MODULE 3: COMMITS, PULL, FETCH AND PUSH
MODULE 4: TAGGING, BRANCHING AND MERGING
MODULE 5: UNDOING CHANGES
MODULE 6: GIT WITH GITHUB AND BITBUCKET
MODULE 1: BIG DATA INTRODUCTION
MODULE 2 : HDFS AND MAP REDUCE
MODULE 3: PYSPARK FOUNDATION
MODULE 4: SPARK SQL and HADOOP HIVE
MODULE 5 : MACHINE LEARNING WITH SPARK ML
MODULE 6: KAFKA and Spark
MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION
MODULE 2: BI WITH TABLEAU: INTRODUCTION
MODULE 3 : TABLEAU: CONNECTING TO DATA SOURCE
MODULE 4: TABLEAU : BUSINESS INSIGHTS
MODULE 5: DASHBOARDS, STORIES AND PAGES
MODULE 6: BI WITH POWER-BI
Data Science is the practice dedicated to extracting valuable insights and knowledge from extensive sets of both structured and unstructured data. It employs a range of techniques, algorithms, and systems to analyze, interpret, and present data in a meaningful way.
The process of Data Science involves the systematic collection, cleaning, and analysis of data to uncover meaningful patterns and trends. Statistical models, machine learning algorithms, and data visualization techniques are often utilized to make informed decisions based on the findings.
Data Science has practical applications in predictive analytics, fraud detection, recommendation systems, sentiment analysis, and optimization of business processes across various industries, showcasing its versatility and importance.
Vital elements of a Data Science pipeline include data collection, data cleaning, exploratory data analysis (EDA), feature engineering, model training, model evaluation, and deployment. These stages collectively contribute to the comprehensive process of deriving insights from data.
Python and R stand out as commonly used programming languages in Data Science. Their popularity is attributed to the extensive libraries and frameworks available, facilitating tasks such as data manipulation, analysis, and the implementation of machine learning algorithms.
Machine learning plays a crucial role in Data Science by empowering systems to discern patterns from data autonomously, allowing for predictions and decisions to be made without explicit programming. This enhances the capacity to extract valuable insights from intricate datasets.
The connection between Big Data and Data Science is intimate, as the latter involves handling and analyzing extensive datasets that conventional data processing tools might struggle to manage. Data Science methodologies and algorithms are often employed to extract meaningful information from the vast expanse of Big Data.
Data Science finds practical application in sectors such as healthcare, finance, marketing, and manufacturing, where it aids in optimizing operations, refining decision-making processes, and enhancing overall business performance.
While Data Science encompasses a broader spectrum of activities, including data cleaning, exploration, and visualization, machine learning specifically concentrates on crafting algorithms that empower systems to learn patterns and make predictions autonomously.
Those eligible to pursue Data Science certification courses come from varied backgrounds, including IT professionals, statisticians, analysts, and business experts. A foundational understanding of statistics and programming proves advantageous for individuals venturing into the realm of Data Science.
As of 2024, the data science job market in Austria is on an upward trajectory, witnessing notable growth and an escalating demand for proficient professionals.
The Certified Data Scientist Course in Austria stands out as a leading option for individuals seeking comprehensive data science training, covering crucial areas like machine learning and data analysis.
In Austria, data science internships hold immense significance, providing hands-on experience and contributing significantly to one's employability within the growing field.
An individual at the entry-level can pursue a data science course and successfully secure a job in Austria, as companies in the region actively seek to hire and onboard skilled newcomers.
No, having a postgraduate degree is not a mandatory requirement for joining data science training courses in Austria; many programs are open to candidates with relevant undergraduate backgrounds.
Businesses in Austria utilize data science to spur growth by refining decision-making processes, streamlining operations, and elevating overall customer experiences.
In the financial sector of Austria, data science finds practical applications in areas such as risk management, fraud detection, and predictive analytics, contributing significantly to the industry's efficiency.
In the context of Austria, data science plays a pivotal role in e-commerce by driving recommendation systems, personalized marketing, and accurate demand forecasting, thus enhancing the overall customer experience.
Within the realm of cybersecurity in Austria, data science assumes a crucial role in detecting anomalies, recognizing patterns, and fortifying threat detection and prevention measures.
In the domains of manufacturing and supply chain management in Austria, data science is instrumental in optimizing production processes, predicting demand, and refining logistics efficiency for enhanced operational performance.
The salary of a data scientist in Austria ranges from EUR 53,801 according to a Glassdoor report.
The Datamites™ Certified Data Scientist course delves into essential facets of data science, encompassing programming, statistics, machine learning, and business knowledge. With a primary focus on Python as the main programming language, it also accommodates professionals familiar with R. The comprehensive curriculum establishes a robust foundation, and successful completion, coupled with the IABAC™ certificate, positions individuals as adept data science professionals ready to tackle industry challenges.
While beneficial, a statistical background is not always mandatory for embarking on a data science career in Austria. Emphasis is often placed on proficiency in relevant tools, programming languages, and practical problem-solving skills.
In Austria, DataMites offers a diverse array of data science certifications, including but not limited to a Diploma in Data Science, Certified Data Scientist, Data Science for Managers, Data Science Associate, Statistics for Data Science, Python for Data Science, and specialized certifications in domains like Marketing, Operations, Finance, and HR.
For those new to the field in Austria, introductory courses like Certified Data Scientist, Data Science Foundation, and Diploma in Data Science provide foundational training in data science.
DataMites in Austria caters to working professionals seeking to elevate their expertise with a variety of courses, including Statistics for Data Science, Data Science with R Programming, Python for Data Science, Data Science Associate, and specialized certifications in Operations, Marketing, HR, and Finance.
The duration of the data science course in Austria spans over a period of 8 months.
Career mentoring sessions at DataMites are engaging and personalized, offering tailored guidance on resume development, interview readiness, and effective career strategies. These sessions aim to provide participants with valuable insights to enrich their professional journey within the realm of data science.
Upon successfully finishing the training, participants receive the esteemed IABAC Certification from DataMites. Widely recognized internationally, this certification serves as a testament to one's proficiency in data science principles and practical applications.
For success in data science, a robust background in mathematics, statistics, and programming is essential. It is advisable to cultivate strong analytical skills, proficiency in languages such as Python or R, and hands-on experience with tools like Hadoop or SQL databases.
Choosing online data science training in Austria provides advantages such as flexibility, accessibility, a well-rounded curriculum aligned with industry requirements, content relevant to the industry, experienced instructors, interactive learning experiences, and the freedom to learn at one's own pace.
The cost of data science training in Austria with DataMites varies between EUR 488 to EUR 1,220 depending on the specific program chosen.
Certainly, DataMites provides a Data Scientist Course in Austria that incorporates practical learning with over 10 capstone projects and a dedicated client/live project. This hands-on approach ensures participants gain real-world experience and practical application of acquired skills.
Trainers at DataMites are chosen based on their certifications, extensive industry experience, and expertise in the subject matter, ensuring participants receive high-quality instruction from seasoned professionals.
DataMites offers versatile learning methods, including Live Online sessions and self-study options, catering to the diverse preferences of participants.
The FLEXI-PASS feature in DataMites' Certified Data Scientist Course allows participants to engage in multiple batches, providing the flexibility to revisit topics, address queries, and reinforce understanding across various sessions for a comprehensive grasp of the course content.
Absolutely, upon successfully completing the DataMites' Data Science Course, participants have the option to request a Certificate of Completion through the online portal. This certification acts as a testament to their data science proficiency, enhancing their standing in the competitive job market.
Certainly, participants are required to bring a valid Photo ID Proof, such as a National ID card or Driving License, to secure a Participation Certificate and facilitate the scheduling of the certification exam as necessary.
In the event of a missed session during the DataMites Certified Data Scientist Course in Austria, participants typically have the option to access recorded sessions or attend support sessions to make up for any missed content and address any queries.
Indeed, potential participants at DataMites are invited to attend a demo class before making any payments for the Certified Data Scientist Course in Austria. This allows them to assess the teaching style, course content, and overall structure before making a commitment.
Certainly, DataMites integrates internships into its certified data scientist course in Austria, offering a distinctive learning experience that combines theoretical knowledge with practical industry exposure. This unique approach enhances skills and opens up job opportunities in the dynamic field of data science.
Upon successful completion of the Data Science training, you will receive an internationally recognized IABAC® certification. This certification validates your expertise in the field, enhancing your employability on a global scale.
The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -
The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.
No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.