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Self Learning + Live Mentoring
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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
The Data Science field offers diverse opportunities, spanning machine learning, statistics, and data analysis, all aimed at deriving insights and supporting informed decision-making.
The intersection of Big Data and Data Science lies in the handling and analysis of large datasets. Big Data emphasizes tools and technologies for managing extensive data volumes, aligning with Data Science objectives.
Coding experience is advantageous, but individuals without it can still enter Data Science using no-code/low-code platforms and other tools.
Typically, a bachelor's or master's degree in fields like computer science, statistics, or mathematics is sought for Data Science roles.
Aspiring Data Scientists should possess skills in programming (especially Python), statistical knowledge, machine learning expertise, data visualization, and strong problem-solving abilities.
Building an effective portfolio involves showcasing real-world projects, highlighting problem-solving skills, and demonstrating proficiency in relevant tools and techniques.
Proficiency in Python is often considered essential for Data Science due to its widespread use in data analysis, machine learning, and building data pipelines.
In Germany, the typical Data Scientist career path may include roles such as Data Analyst, Junior Data Scientist, Senior Data Scientist, with potential progression to managerial positions.
Data Science Certification Courses are generally open to individuals with a background in mathematics, statistics, computer science, or related fields.
Initial steps for entering the Data Science field in Germany include gaining foundational knowledge, acquiring relevant technical skills, and networking with local professionals and organizations.
Compensation for Data Scientists in Germany varies, influenced by experience, skills, and industry, with an average salary range of EUR 70,290 annually.
To create an impactful portfolio for a Data Science position, showcase diverse projects, highlight technical skills, and include clear explanations of methodologies and outcomes.
High demand for Data Scientists is observed in tech hubs like Silicon Valley, financial centers, and healthcare sectors globally.
Emerging trends in Data Science include explainable AI, automated machine learning, and an increased focus on ethical considerations in AI applications.
A postgraduate degree is not always necessary for data science training in Germany; many programs accept candidates with relevant experience and skills.
The Data Science workflow includes data collection, cleaning, exploration, modeling, validation, and deployment, with iterative steps for continuous improvement.
Data Science in Germany contributes to business growth by enhancing decision-making, providing customer insights, and optimizing operations, ultimately increasing competitiveness.
The Certified Data Scientist Course is a top recommendation for data science training in Germany, covering essential topics like machine learning and data analysis.
Data Science is utilized across various industries, including finance, healthcare, e-commerce, and telecommunications, with applications in predictive analytics, fraud detection, and personalized marketing.
Data Science focuses on deriving insights from data, while Machine Learning, as a subset, involves training models to make predictions or decisions based on data.
Proficiency in Python is frequently deemed essential for entering the Data Science domain due to its widespread use in data analysis, machine learning, and the construction of data pipelines.
In Berlin, the typical trajectory for a Data Scientist's career may encompass roles such as Data Analyst, Junior Data Scientist, and Senior Data Scientist, with potential progression into managerial positions.
The DataMites Certified Data Scientist Course in Germany is a globally recognized program focusing on Data Science and Machine Learning. It undergoes regular updates to align with industry demands, providing a structured learning experience for efficient and targeted education.
Certainly, DataMites in Germany presents a range of data science certifications, including 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 courses in Marketing, Operations, Finance, and HR.
For newcomers in Germany venturing into data science, entry-level training options encompass courses like Certified Data Scientist, Data Science in Foundation, and Diploma in Data Science.
DataMites in Germany caters to working professionals with specialized courses such as Statistics for Data Science, Data Science with R Programming, Python for Data Science, Data Science Associate, and certifications in Operations, Marketing, HR, and Finance.
The duration of DataMites' data scientist course in Germany ranges from 1 month to 8 months, depending on the specific course level.
Enrollment in Certified Data Scientist Training in Germany is open to beginners and intermediate learners in data science, with no prerequisites required.
Opting for online data science training in Germany from DataMites provides benefits such as flexibility, accessibility, a comprehensive curriculum, industry-relevant content, expert instructors, and interactive learning experiences.
DataMites' data science training in Germany has a fee structure ranging from EUR 488 to 1,220 ensuring affordable options for individuals to access quality education in the field of data science.
Instructors at DataMites are chosen based on certifications, extensive industry experience, and mastery of the subject matter to ensure high-quality training sessions.
Yes, participants are required to bring a valid Photo ID Proof, such as a National ID card or Driving License, to obtain a Participation Certificate and schedule the certification exam as needed.
In the DataMites Certified Data Scientist Course in Germany, participants have the flexibility to access recorded sessions or engage in support sessions if they miss a class. This ensures learners can review missed content, clarify uncertainties, and stay aligned with the course curriculum.
Prospective participants in the Certified Data Scientist Course in Germany have the opportunity to attend a demonstration class before making any financial commitment. This allows them to evaluate teaching styles, course content, and overall structure, making informed enrollment decisions.
DataMites integrates internships into its certified data scientist course in Germany, offering a distinctive learning experience combining theoretical knowledge with practical industry exposure. This enhances skills and job opportunities in the dynamic field of data science.
Tailored for managers and leaders, the "Data Science for Managers" course at DataMites is designed to meet their specific needs. This course equips them with essential skills to seamlessly integrate data science into decision-making processes, facilitating well-informed and strategic choices.
Certainly, individuals in Germany participating in the program have the choice to attend help sessions, providing a valuable opportunity for a more in-depth understanding of specific data science topics. This ensures a thorough learning experience and addresses individual queries effectively.
Indeed, DataMites' Data Scientist Course in Germany includes hands-on learning with over 10 capstone projects and a dedicated client/live project. This practical experience enhances participants' skills by providing real-world applications and industry-relevant exposure.
Certainly, upon successful completion of the course, participants can request the Data Science Course Completion Certificate through the online portal. This certificate validates their proficiency in data science, enhancing credibility in the job market.
The Flexi-Pass feature in DataMites' Certified Data Scientist Course allows participants to enroll in multiple batches, providing flexibility to revisit topics, address uncertainties, and deepen comprehension through various sessions. This ensures a comprehensive and personalized learning experience.
DataMites' career mentoring sessions follow an interactive format, offering personalized guidance on resume building, interview preparation, and career strategies. These sessions provide valuable insights and effective strategies to elevate participants' professional journey in the field of data science.
DataMites in Germany provides live online training, facilitating real-time interaction with instructors and creating an engaging and interactive learning environment for participants. Self-Paced Training: Participants can access recorded sessions at their convenience, allowing for a personalized learning pace and accommodating diverse schedules to optimize learning outcomes.
Upon completing the Data Science training, you will receive an internationally recognized IABAC® certification. This certification validates your expertise in the field and enhances 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.