Instructor Led Live Online
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 unfolds a myriad of opportunities, encompassing machine learning, statistics, and data analysis, all directed towards extracting insights and facilitating well-informed decision-making.
The convergence of Big Data and Data Science occurs in the handling and analysis of extensive datasets. Big Data focuses on tools and technologies tailored for managing vast data volumes, aligning with the objectives of Data Science.
While coding experience is beneficial, those without it can still embark on a Data Science journey using no-code/low-code platforms and other accessible tools.
Roles in Data Science generally seek candidates with a bachelor's or master's degree in fields such as computer science, statistics, or mathematics.
Aspiring Data Scientists should wield skills in programming, particularly in Python, possess statistical acumen, demonstrate proficiency in machine learning, showcase data visualization capabilities, and exhibit strong problem-solving skills.
Crafting an impactful portfolio involves showcasing real-world projects, emphasizing problem-solving prowess, and illustrating proficiency in relevant tools and techniques.
Certification Courses for Data Science generally welcome individuals with a background in mathematics, statistics, computer science, or related fields.
The preliminary steps to enter the Data Science field in Berlin involve acquiring foundational knowledge, developing relevant technical skills, and establishing connections with local professionals and organizations.
Data Scientists in Berlin receive varying compensation influenced by factors like experience, skills, and industry, with an average annual salary range of EUR 70,290.
Crafting a compelling portfolio for a Data Science role involves showcasing diverse projects, emphasizing technical skills, and providing clear explanations of methodologies and outcomes.
A surge in demand for Data Scientists is observed in global tech hubs like Silicon Valley, financial centers, and the healthcare sector.
Emerging trends in Data Science include the rise of explainable AI, the automation of machine learning processes, and an increased emphasis on ethical considerations in AI applications.
Enrolling in Data Science training programs in Berlin does not universally require a postgraduate degree, as many programs welcome candidates with relevant experience and skills.
The Data Science workflow encompasses stages like data collection, cleaning, exploration, modeling, validation, and deployment, characterized by iterative steps for continuous improvement.
In Berlin, Data Science contributes to business expansion by elevating decision-making processes, offering valuable customer insights, and optimizing operational efficiency, ultimately enhancing competitiveness.
The Certified Data Scientist Course stands out as a top recommendation for individuals seeking data science training in Berlin, covering crucial topics such as machine learning and data analysis.
Data Science finds applications across diverse sectors, including finance, healthcare, e-commerce, and telecommunications. Its practical applications encompass predictive analytics, fraud detection, and personalized marketing.
Data Science revolves around deriving insights from data, whereas Machine Learning, as a subset, focuses on training models to make predictions or decisions based on data.
The DataMites Certified Data Scientist Course in Berlin is a globally recognized program that delves into Data Science and Machine Learning. It undergoes regular updates to stay in sync with industry requirements, providing a structured and effective learning experience.
Certainly, DataMites in Berlin offers a variety of data science certifications, including the 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 those new to data science in Berlin, entry-level training options include courses such as Certified Data Scientist, Data Science in Foundation, and Diploma in Data Science.
Indeed, DataMites in Berlin caters to working professionals with specialized courses like 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 Berlin varies from 1 month to 8 months, depending on the specific level of the course.
Enrollment in Certified Data Scientist Training in Berlin is open to beginners and intermediate learners in data science, with no specific prerequisites required.
Opting for online data science training in Berlin from DataMites in Berlin offers advantages such as flexibility, accessibility, a comprehensive curriculum, industry-relevant content, expert instructors, and engaging learning experiences.
DataMites' data science training fees in Berlin range from EUR 488 to EUR 1,220, ensuring affordability and accessibility for individuals seeking quality education in the field.
Instructors at DataMites are selected based on certifications, extensive industry experience, and mastery of the subject matter, ensuring the delivery of high-quality training sessions.
Certainly, participants must 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 if necessary.
In the DataMites Certified Data Scientist Course in Berlin, participants have the flexibility to access recorded sessions or participate in support sessions if they miss a class. This ensures learners can review missed content, clarify uncertainties, and stay aligned with the course curriculum.
Certainly, individuals considering the Certified Data Scientist Course in Berlin have the opportunity to attend a demonstration class before making any financial commitment. This allows them to assess teaching styles, course content, and overall structure, aiding informed enrollment decisions.
DataMites integrates internships into its certified data scientist course in Berlin, providing a unique learning experience that combines 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 Berlin 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 Berlin 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 Berlin 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.