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 at the crossroads 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 encompass a systematic process involving the collection, cleansing, and analysis of data to unveil patterns and insights. This process aids in decision-making and addresses intricate problems.
Data Science finds practical implementations across various sectors such as finance, healthcare, marketing, and technology. It addresses challenges like fraud detection, personalized medicine, and customer analytics.
Essential stages in a Data Science pipeline include data collection, cleansing, exploratory data analysis, feature engineering, model training, evaluation, and deployment.
In the domain of machine learning, pivotal languages like Python play a crucial role, contributing to tasks such as classification, regression, and clustering.
The integration of machine learning into Data Science involves crafting models capable of learning from data, enabling predictions or decisions across a wide range 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 like finance leverage Data Science for risk analysis, healthcare employs it for predictive modeling, and retail utilizes it for demand forecasting, showcasing the versatile applications of this field.
While Data Science encompasses a broader range of tasks, including data analysis, machine learning specifically focuses on constructing models that learn from data to make predictions.
Individuals with backgrounds in mathematics, statistics, computer science, or related fields, combined with a keen interest in data analysis, are eligible to pursue certification courses in Data Science.
While proficiency in Python is commonly expected in data science, certain roles may consider proficiency in alternative languages, recognizing the valuable skills and extensive support offered by Python.
Crafting a compelling data science portfolio involves presenting projects with well-defined problem statements, thorough exploration, analysis, and visualization of data, accompanied by detailed explanations of methodologies and discoveries.
Transitioning from a non-coding background to data science is achievable through dedication, self-learning, and relevant courses. A recommended approach involves starting with foundational coding skills and progressively delving into advanced topics.
While various 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 are highly valued.
Critical skills for a Data Scientist include proficiency in programming languages (such as Python), statistical knowledge, expertise in machine learning, adept data wrangling abilities, and effective communication.
Building a strong data science portfolio involves actively participating in real-world projects, engaging in online competitions, and consistently refining and updating skills to effectively showcase expertise.
Industries actively seeking Data Scientists include finance, healthcare, technology, e-commerce, and telecommunications, emphasizing the diverse and widespread demand for data science expertise.
Emerging trends in Data Science include the rise of automated machine learning, an increased focus on explainable AI, and a growing awareness of ethical considerations in data usage.
The typical career progression for a Data Scientist in Oslo involves starting as a Junior Data Scientist, progressing to a Data Scientist role, and potentially moving into leadership positions like Lead Data Scientist or Data Science Manager.
Initiating a data science career in Oslo involves acquiring relevant skills, networking with professionals, actively participating in local events, and seeking internships or entry-level positions in companies focusing on data science.
The salary of a data scientist in Oslo ranges from NOK 7,52,484 per year according to a Glassdoor report.
Datamites™ Certified Data Scientist course is thoughtfully designed to encompass critical aspects of data science, incorporating programming, statistics, machine learning, and business knowledge. Utilizing Python as the primary language, the course accommodates professionals familiar with R, offering a comprehensive foundation and addressing contemporary data science themes. Successful completion, culminating in the IABAC™ certificate, positions individuals as skilled data science professionals poised to meet industry challenges.
While advantageous, a statistical background is not always mandatory for a data science career in Oslo. Proficiency in tools, programming languages, and effective problem-solving often takes precedence in the hiring process.
In Oslo, DataMites presents 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.
For newcomers in Oslo, introductory training options like Certified Data Scientist, Data Science Foundation, and Diploma in Data Science courses are available, offering a starting point for their venture into the field.
DataMites in Oslo tailors courses for working professionals, 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 provided by DataMites in Oslo extends over 8 months, ensuring participants receive a thorough and immersive learning experience.
Career mentoring sessions at DataMites adopt an interactive format, offering personalized guidance on resume building, interview preparation, and career strategies. These sessions are designed to empower participants with valuable insights for their professional journey in the data science field.
Upon successful completion of DataMites' Data Science Training in Oslo, 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 Oslo, individuals are advised to possess 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 Oslo from DataMites offers 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 fees for data science training in Oslo with DataMites range from NOK 5,555 to NOK 13,891, depending on the selected program.
Practical learning is emphasized in the Data Scientist Course in Oslo, with DataMites incorporating over 10 capstone projects and a dedicated client/live project, ensuring participants gain hands-on experience and exposure to industry-relevant scenarios.
DataMites ensures quality training sessions by selecting instructors based on certifications, extensive industry experience, and subject matter expertise.
DataMites provides flexible learning options, including Live Online sessions and self-study, catering to the diverse preferences and learning styles of participants.
The FLEXI-PASS feature in DataMites' Certified Data Scientist Course allows participants to join multiple batches, enabling them to review topics, address doubts, and comprehensively understand the course content.
Upon successful completion, participants receive a Certificate of Completion from DataMites, validating their competence in data science concepts and applications.
Participants are required to bring valid Photo ID Proof, such as a National ID card or Driving License, to receive a Participation Certificate and schedule certification exams.
In case of missed sessions, 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 Oslo, providing an opportunity to assess teaching styles and course content.
Yes, DataMites integrates internships into its certified data scientist course in Oslo, enriching participants' skills with practical industry exposure and creating avenues for job opportunities.
Upon successful completion, participants receive an internationally recognized IABAC® certification, affirming their expertise in data science and enhancing their employability globally.
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.