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
Data Science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data.
Data Science involves collecting, cleaning, analyzing, and interpreting data to uncover patterns, trends, and insights that inform decision-making and solve complex problems.
Data Science finds applications in various domains, including finance, healthcare, marketing, and technology, addressing challenges such as fraud detection, personalized medicine, and customer analytics.
The key components of a Data Science pipeline are -
Machine learning, a subset of Data Science, involves building models that learn from data to make predictions or decisions, contributing to tasks like classification, regression, and clustering.
Big Data involves handling massive datasets, and Data Science often leverages Big Data technologies to analyze and extract meaningful insights from large-scale data.
Big Data involves handling massive datasets, and Data Science often leverages Big Data technologies to analyze and extract meaningful insights from large-scale data.
Industries like finance use Data Science for risk analysis, healthcare for predictive modeling, and retail for demand forecasting, showcasing its versatile applications.
Data Science encompasses a broader range of tasks, including data analysis and visualization, while machine learning specifically focuses on building models that learn from data.
Individuals with a background in mathematics, statistics, computer science, or related fields, along with a curiosity for data analysis, can pursue Data Science certification courses.
Proficiency in Python is commonly required for data science, but some roles may accept other languages. It's a valuable skill due to its extensive libraries and community support.
Create a data science portfolio by showcasing projects with clear problem statements, data exploration, analysis, and visualization, along with explanations of your approach and findings.
Switching from a non-coding background to data science is possible with dedication, self-learning, and relevant courses. Start with basic coding skills and progress to more advanced topics.
A diverse educational background is acceptable; common degrees include computer science, statistics, mathematics, or related fields. However, practical skills and experience often weigh more in the hiring process.
Essential skills for a Data Scientist include programming (e.g., Python), statistical knowledge, machine learning, data wrangling, and effective communication.
Build a strong data science portfolio by working on real-world projects, participating in online competitions, and continuously updating and improving your skills.
Industries actively hiring Data Scientists include finance, healthcare, technology, e-commerce, and telecommunications.
Emerging trends in data science include automated machine learning, explainable AI, and increased focus on ethical considerations in data usage.
The career path for a Data Scientist in Serbia typically involves starting as a Junior Data Scientist, progressing to a Data Scientist, and potentially moving into roles like Lead Data Scientist or Data Science Manager.
Start a career in data science in Serbia by acquiring relevant skills, networking with professionals, participating in local meetups or events, and applying for internships or entry-level positions in companies with a data science focus.
The Datamites™ Certified Data Scientist course is meticulously designed to encompass the essential facets of data science, incorporating a balanced approach across programming, statistics, machine learning, and business knowledge. Emphasizing Python as the core programming language for data science, the course also includes R to cater to professionals familiar with that language. By providing a comprehensive foundation and covering the latest data science topics, this course equips candidates with in-depth knowledge. Successful completion, coupled with the IABAC™ certificate, positions individuals to thrive as competent data science professionals, well-prepared for the demands of the field.
A background in statistics is beneficial but not always essential for a data science career in Serbia; proficiency in relevant tools, programming languages, and practical problem-solving skills are often prioritized.
Novice individuals in Serbia seeking entry-level training in data science can explore options such as the Certified Data Scientist, Data Science Foundation, and Diploma in Data Science courses.
Certainly, DataMites in Serbia offers a diverse range of courses designed for professionals aiming to bolster their expertise. These include Statistics for Data Science, Data Science with R Programming, Python for Data Science, Data Science Associate, as well as specialized certifications in Operations, Marketing, HR, and Finance.
The duration of the course spans 8 months for the data science course in Serbia.
The career mentoring sessions at DataMites adopt an interactive format, delivering personalized guidance on resume construction, interview preparation, and career strategies. These sessions provide valuable insights and tactics to enrich participants' professional journeys within the data science field.
Upon successful completion of DataMites' Data Science Training in Serbia, participants are awarded the esteemed IABAC Certification. This globally recognized certification serves as a testament to their proficiency in data science concepts and practical applications. Functioning as a valuable credential, it validates their expertise and boosts their credibility within the realm of data science.
To excel in data science, build a robust foundation in math, statistics, and programming, develop strong analytical skills, attain proficiency in languages like Python or R, and gain hands-on experience with large datasets and relevant tools like Hadoop or SQL databases.
The data science training fee in Serbia ranges from RSD 51,858 to RSD 143,309 respectively.
Indeed, DataMites offers a Data Scientist Course in Serbia that integrates practical learning through more than 10 capstone projects and a dedicated client/live project. This hands-on experience enhances participants' skills, providing real-world applications and industry-relevant exposure.
We are committed to delivering instructors who hold certifications, possess extensive industry experience spanning decades, and demonstrate expertise in the subject matter.
DataMites offers flexible learning methods, including Live Online sessions and self-study, tailored to accommodate your preferences.
The FLEXI-PASS option in DataMites' Certified Data Scientist Course offers participants the flexibility to join multiple batches, enabling them to review topics, address doubts, and solidify comprehension across various sessions for a comprehensive understanding of the course content.
Certainly, DataMites provides a Certificate of Completion for their Data Science Course. Upon successful course completion, participants can choose to request the certificate via the online portal. This certification affirms their expertise in data science, thereby bolstering credibility in the job market.
Certainly. A valid Photo ID Proof, such as a National ID card or Driving License, is necessary for obtaining a Participation Certificate and scheduling the certification exam as needed.
In case of a missed session in the DataMites Certified Data Scientist Course in Serbia, participants usually have the option to access recorded sessions or attend support sessions. This ensures learners can make up for missed content, clarify doubts, and stay aligned with the course curriculum.
Indeed, potential participants at DataMites can take a demo class before making any payment for the Certified Data Scientist Course in Serbia. This provides individuals with an opportunity to assess the teaching style, course content, and overall structure, enabling them to make an informed decision regarding enrollment.
DataMites distinguishes itself by incorporating internships into its certified data scientist course in Serbia, providing a distinctive learning experience that combines theoretical knowledge with practical industry exposure. The added advantage of earning a data science certification from an AI company enhances skills and elevates job opportunities in the ever-evolving field of data science.
Upon completing the Data Science training, you will be granted an internationally recognized IABAC® certification, affirming your proficiency in the field and boosting 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.