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 operates within the convergence of diverse disciplines, employing scientific methodologies, algorithms, and systems to extract valuable insights from both structured and unstructured data.
The operational dynamics of Data Science involve a methodical process of collecting, cleansing, and analyzing data to reveal patterns and insights, aiding in decision-making and addressing complex problems.
Practical implementations of Data Science extend across various sectors, including finance, healthcare, marketing, and technology, addressing challenges such as fraud detection, personalized medicine, and customer analytics.
Integral stages in a Data Science pipeline encompass data collection, cleansing, exploratory data analysis, feature engineering, model training, evaluation, and deployment.
In the realm of machine learning, a subset of Data Science, pivotal languages like Python play a crucial role, contributing to tasks such as classification, regression, and clustering.
The incorporation of machine learning into Data Science involves the creation of models capable of learning from data, enabling predictions or decisions across a spectrum of tasks and applications.
Big Data, characterized by extensive datasets, closely intersects with Data Science, leveraging advanced technologies to extract insights and patterns from vast amounts of data.
Industries such as finance leverage Data Science for risk analysis, healthcare employs it for predictive modeling, and retail utilizes it for demand forecasting, highlighting the versatile applications of this field.
While Data Science encompasses a broader array of tasks, including data analysis, machine learning specifically concentrates on constructing models that learn from data to make predictions.
Individuals with backgrounds in mathematics, statistics, computer science, or related fields, coupled with a keen interest in data analysis, are eligible to pursue certification courses in Data Science.
While proficiency in Python is often a requirement in data science, certain roles may consider expertise in alternative languages, acknowledging the valuable skills and extensive support provided by Python.
Developing an impactful data science portfolio entails presenting projects with clearly defined problem statements, thorough exploration, analysis, and visualization of data, complemented by detailed explanations of methodologies and discoveries.
Transitioning from a non-coding background to data science is feasible through commitment, self-learning, and relevant courses. A recommended approach involves starting with foundational coding skills and progressively delving into advanced topics.
While diverse educational backgrounds are acceptable, degrees in computer science, statistics, mathematics, or related fields are commonly sought for a career in Data Science. Practical skills and hands-on experience carry significant weight.
Critical skills for a Data Scientist encompass proficiency in programming languages (such as Python), statistical knowledge, expertise in machine learning, adept data wrangling abilities, and effective communication.
Building a robust data science portfolio involves actively engaging in real-world projects, participating in online competitions, and consistently honing and updating skills to showcase one's expertise effectively.
Industries actively seeking Data Scientists include finance, healthcare, technology, e-commerce, and telecommunications, underscoring the diverse and widespread demand for data science expertise.
Emerging trends in Data Science encompass the ascendancy of automated machine learning, a heightened emphasis on explainable AI, and an increased awareness of ethical considerations in the use of data.
The typical career path for a Data Scientist in Norway involves commencing as a Junior Data Scientist, advancing to a Data Scientist role, and potentially ascending to leadership positions such as Lead Data Scientist or Data Science Manager.
Commencing a career in data science in Norway involves acquiring relevant skills, networking with professionals, actively participating in local events, and seeking internships or entry-level positions in companies with a focus on data science.
The salary of a data scientist in Norway ranges from NOK 6,39,555 per year according to a Glassdoor report.
Datamites™ Certified Data Scientist course is meticulously crafted to cover crucial aspects of data science, including programming, statistics, machine learning, and business knowledge. With Python as the core language, the course accommodates professionals familiar with R, providing a comprehensive foundation and addressing contemporary data science topics. Successful completion, crowned 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 a data science career in Norway. Proficiency in tools, programming languages, and problem-solving skills often takes precedence in the hiring process.
In Norway, DataMites offers a diverse array of programs, including the Diploma in Data Science, Certified Data Scientist, Data Science for Managers, Data Science Associate, and specialized certifications in Operations, Marketing, HR, and Finance.
Newcomers in Norway can explore introductory training options like Certified Data Scientist, Data Science Foundation, and Diploma in Data Science courses to kickstart their journey into the field.
Certainly, DataMites in Norway caters to working professionals with specialized courses, including Statistics for Data Science, Data Science with R Programming, Python for Data Science, and certifications in Operations, Marketing, HR, and Finance.
The data science course offered by DataMites in Norway spans 8 months, providing participants with a comprehensive and immersive learning experience.
Career mentoring sessions at DataMites follow an interactive structure, offering personalized guidance on resume building, interview preparation, and career strategies. These sessions aim to equip participants with valuable insights to enhance their professional journey in the field of data science.
Upon successful completion of DataMites' Data Science Training in Norway, participants receive the globally recognized IABAC Certification, validating their proficiency in various data science concepts and applications.
To excel in Certified Data Scientist Training in Norway, individuals are recommended to have a strong foundation in mathematics, statistics, programming, analytical skills, proficiency in Python or R, and hands-on experience with tools like Hadoop or SQL databases.
Online data science training in Norway from DataMites provides flexibility, overcoming geographical constraints, delivering a comprehensive syllabus, industry-relevant content, skilled instructors, and engaging learning experiences tailored to meet the demands of the field.
The data science training fees in Norway range from NOK 5,555 to NOK 13,891 depending on the specific program chosen.
DataMites ensures practical learning by integrating over 10 capstone projects and a dedicated client/live project into the Data Scientist Course in Norway, providing participants with hands-on experience and industry-relevant exposure.
Instructors at DataMites are selected based on certifications, extensive industry experience, and expertise in the subject matter, ensuring high-quality training sessions.
DataMites offers flexible learning methods, including Live Online sessions and self-study options, tailored to accommodate participants' preferences and learning styles.
The FLEXI-PASS option in DataMites' Certified Data Scientist Course allows participants to join multiple batches, facilitating review sessions, doubt clarification, and a comprehensive understanding of the course content.
Upon successful completion of data science classes, participants are awarded a Certificate of Completion from DataMites, validating their proficiency in data science concepts and applications.
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.
In the event of a missed session, participants typically have access to recorded sessions or support sessions to catch up on content and clarify any doubts.
Certainly, prospective participants can attend a demo class before making any payment for the Certified Data Scientist Course in Norway, allowing them to assess the teaching style and course content.
Yes, DataMites integrates internships into its certified data scientist course in Norway, providing participants with practical industry exposure to enhance their skills and create job opportunities.
Upon successful completion, participants receive an internationally recognized IABAC® certification, affirming their proficiency in data science and boosting their 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.