DATA ANALYST CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ANALYST LEAD MENTORS

DATA ANALYST COURSE FEE IN STOCKHOLM, SWEDEN

Live Virtual

Instructor Led Live Online

SEK 17,350
SEK 9,839

  • IABAC® Certification
  • 6-Month | 200+ Learning Hours
  • 20 HOURS LEARNING A WEEK
  • 10 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

SEK 8,670
SEK 5,636

  • Self Learning + Live Mentoring
  • IABAC® Certification
  • 1 Year Access To Elearning
  • 10 Capstone & 1 Client Project
  • Job Assistance
  • 24*7 Learner assistance and support

Corporate Training

Customize Your Training


  • Instructor-Led & Self-Paced training
  • Customized Learning Options
  • Industry Expert Trainers
  • Case Study Approach
  • Enterprise Grade Learning
  • 24*7 Cloud Lab

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UPCOMING DATA ANALYST ONLINE CLASSES IN STOCKHOLM

BEST DATA ANALYTICS CERTIFICATIONS

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.

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WHY DATAMITES INSTITUTE FOR DATA ANALYST COURSE

Why DataMites Infographic

SYLLABUS OF DATA ANALYST COURSE IN STOCKHOLM

MODULE 1: DATA ANALYSIS FOUNDATION

• Data Analysis Introduction
• Data Preparation for Analysis
• Common Data Problems
• Various Tools for Data Analysis
• Evolution of Analytics domain

MODULE 2: CLASSIFICATION OF ANALYTICS

• Four types of the Analytics
• Descriptive Analytics
• Diagnostics Analytics
• Predictive Analytics
• Prescriptive Analytics
• Human Input in Various type of Analytics

MODULE 3: CRIP-DM Model

• Introduction to CRIP-DM Model
• Business Understanding
• Data Understanding
• Data Preparation
Modeling, Evaluation, Deploying,Monitoring

MODULE 4: UNIVARIATE DATA ANALYSIS

• Summary statistics -Determines the value’s center and spread.
• Measure of Central Tendencies: Mean, Median and Mode
• Measures of Variability: Range, Interquartile range, Variance and Standard Deviation
• Frequency table -This shows how frequently various values occur.
• Charts -A visual representation of the distribution of values.

MODULE 5: DATA ANALYSIS WITH VISUAL CHARTS

• Line Chart
• Column/Bar Chart
• Waterfall Chart
• Tree Map Chart
• Box Plot

MODULE 6: BI-VARIATE DATA ANALYSIS

• Scatter Plots
• Regression Analysis
• Correlation Coefficients

MODULE 1: PYTHON BASICS

• Introduction of python
• Installation of Python and IDE
• Python Variables
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators

MODULE 2: PYTHON CONTROL STATEMENTS

• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements

MODULE 3: PYTHON DATA STRUCTURES

• Basic data structure in python
• Basics of List
• List: Object, methods
• Tuple: Object, methods
• Sets: Object, methods
• Dictionary: Object, methods

MODULE 4: PYTHON FUNCTIONS

• Functions basics
• Function Parameter passing
• Lambda functions
• Map, reduce, filter functions

MODULE 1 : OVERVIEW OF STATISTICS 

  • Introduction to Statistics
  • Descriptive And Inferential Statistics
  • Basic Terms Of Statistics
  • Types Of Data

MODULE 2 : HARNESSING DATA 

  • Random Sampling
  • Sampling With Replacement And Without Replacement
  • Cochran's Minimum Sample Size
  • Types of Sampling
  • Simple Random Sampling
  • Stratified Random Sampling
  • Cluster Random Sampling
  • Systematic Random Sampling
  • Multi stage Sampling
  • Sampling Error
  • Methods Of Collecting Data

MODULE 3 : EXPLORATORY DATA ANALYSIS 

  • Exploratory Data Analysis Introduction
  • Measures Of Central Tendencies: Mean, Median And Mode
  • Measures Of Central Tendencies: Range, Variance And Standard Deviation
  • Data Distribution Plot: Histogram
  • Normal Distribution & Properties
  • Z Value / Standard Value
  • Empherical Rule  and Outliers
  • Central Limit Theorem
  • Normality Testing
  • Skewness & Kurtosis
  • Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance
  • Covariance & Correlation

MODULE 4 : HYPOTHESIS TESTING 

  • Hypothesis Testing Introduction
  • P- Value, Critical Region
  • Types of Hypothesis Testing
  • Hypothesis Testing Errors : Type I And Type Ii
  • Two Sample Independent T-test
  • Two Sample Relation T-test
  • One Way Anova Test
  • Application of Hypothesis testing

MODULE 1: COMPARISION AND CORRELATION ANALYSIS

• Data comparison Introduction,
• Performing Comparison Analysis on Data
• Concept of Correlation
• Calculating Correlation with Excel
• Comparison vs Correlation
• Hands-on case study : Comparison Analysis
• Hands-on case study Correlation Analysis

MODULE 2: VARIANCE AND FREQUENCY ANALYSIS

• Variance Analysis Introduction
• Data Preparation for Variance Analysis
• Performing Variance and Frequency Analysis
• Business use cases for Variance Analysis
• Business use cases for Frequency Analysis

MODULE 3: RANKING ANALYSIS

• Introduction to Ranking Analysis
• Data Preparation for Ranking Analysis
• Performing Ranking Analysis with Excel
• Insights for Ranking Analysis
• Hands-on Case Study: Ranking Analysis

MODULE 4: BREAK EVEN ANALYSIS

• Concept of Breakeven Analysis
• Make or Buy Decision with Break Even
• Preparing Data for Breakeven Analysis
• Hands-on Case Study: Manufacturing

MODULE 5: PARETO (80/20 RULE) ANALSYSIS

• Pareto rule Introduction
• Preparation Data for Pareto Analysis,
• Performing Pareto Analysis on Data
• Insights on Optimizing Operations with Pareto Analysis
• Hands-on case study: Pareto Analysis

MODULE 6: Time Series and Trend Analysis

• Introduction to Time Series Data
• Preparing data for Time Series Analysis
• Types of Trends
• Trend Analysis of the Data with Excel
• Insights from Trend Analysis

MODULE 7: DATA ANALYSIS BUSINESS REPORTING

• Management Information System Introduction
• Various Data Reporting formats
• Creating Data Analysis reports as per the requirements

MODULE 1: DATA ANALYTICS FOUNDATION

• Business Analytics Overview
• Application of Business Analytics
• Benefits of Business Analytics
• Challenges
• Data Sources
• Data Reliability and Validity

MODULE 2: OPTIMIZATION MODELS

• Predictive Analytics with Low Uncertainty;Case Study
• Mathematical Modeling and Decision Modeling
• Product Pricing with Prescriptive Modeling
• Assignment 1 : KERC Inc, Optimum Manufacturing Quantity

MODULE 3: PREDICTIVE ANALYTICS WITH REGRESSION

• Mathematics behind Linear Regression
• Case Study : Sales Promotion Decision with Regression Analysis
• Hands on Regression Modeling in Excel

MODULE 4: DECISION MODELING

• Predictive Analytics with High Uncertainty
• Case Study-Monte Carlo Simulation
• Comparing Decisions in Uncertain Settings
• Trees for Decision Modeling
• Case Study : Supplier Decision Modeling - Kickathlon Sports Retailer

MODULE 1: MACHINE LEARNING INTRODUCTION

• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification And Regression
• Supervised Vs Unsupervised

MODULE 2: ML ALGO: LINEAR REGRESSSION

• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Hands-on Linear Regression with ML Tool

MODULE 3: ML ALGO: LOGISTIC REGRESSION

• Introduction to Logistic Regression;
• Classification & Sigmoid Curve
• Hands-on Logistics Regression with ML Tool

MODULE 4: ML ALGO: KNN

• Introduction to KNN; Nearest Neighbor
• Regression with KNN
• Hands-on: KNN with ML Tool

MODULE 5: ML ALGO: K MEANS CLUSTERING

• Understanding Clustering (Unsupervised)
• Introduction to KMeans and How it works
• Hands-on: K Means Clustering

MODULE 6: ML ALGO: DECISION TREE

• Decision Tree and How it works
• Hands-on: Decision Tree with ML Tool

MODULE 7: ML ALGO: SUPPORT VECTOR MACHINE (SVM)

• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Hands-on: SVM with ML Tool

MODULE 8: ARTIFICIAL NEURAL NETWORK (ANN)

• Introduction to ANN, How It Works
• Back propagation, Gradient Descent
• Hands-on: ANN with ML Tool

MODULE 1: DATABASE INTRODUCTION

• DATABASE Overview
• Key concepts of database management
• CRUD Operations
• Relational Database Management System
• RDBMS vs No-SQL (Document DB)

MODULE 2: SQL BASICS

• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation

MODULE 3: DATA TYPES AND CONSTRAINTS

• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment

MODULE 4: DATABASES AND TABLES (MySQL)

• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create new table from existing data types
• Insert into, Update records
• Alter table

MODULE 5: SQL JOINS

• Inner join, Outer Join
• Left join, Right Join
• Self Join, Cross join
• Windows Functions: Over, Partition, Rank

MODULE 6: SQL COMMANDS AND CLAUSES

• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries

MODULE 7: DOCUMENT DB/NO-SQL DB

• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods
• MongoDB data management

MODULE 1: BIG DATA INTRODUCTION

• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction

MODULE 2: HDFS AND MAP REDUCE

• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing stages concepts
• Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort

MODULE 3: PYSPARK FOUNDATION

• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs

MODULE 4: SPARK SQL and HADOOP HIVE

• Introducing Spark SQL
• Spark SQL vs Hadoop Hive

MODULE 1: TABLEAU FUNDAMENTALS

• Introduction to Business Intelligence & Introduction to Tableau
• Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
• Bar chart, Tree Map, Line Chart
• Area chart, Combination Charts, Map
• Dashboards creation, Quick Filters
• Create Table Calculations
• Create Calculated Fields
• Create Custom Hierarchies

MODULE 2: POWER-BI BASICS

• Power BI Introduction
• Basics Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX FUNCTION

MODULE 3: DATA TRANSFORMATION TECHNIQUES

• Exploring Query Editor
• Data Cleansing and Manipulation:
• Creating Our Initial Project File
• Connecting to Our Data Source
• Editing Rows
• Changing Data Types
• Replacing Values

MODULE 4: CONNECTING TO VARIOUS DATA SOURCES

• Connecting to a CSV File
• Connecting to a Webpage
• Extracting Characters
• Splitting and Merging Columns
• Creating Conditional Columns
• Creating Columns from Examples
• Create Data Model

OFFERED DATA ANALYST COURSES IN STOCKHOLM

DATA ANALYST COURSE REVIEWS

ABOUT DATA ANALYST TRAINING IN STOCKHOLM

The Data Analytics Market is on the verge of remarkable expansion, projected to achieve a noteworthy valuation of USD 162.03 billion by 2030, exhibiting substantial growth from the recorded USD 41.22 billion in 2022. This signifies a Compound Annual Growth Rate (CAGR) of 18.66% throughout the forecast period. The escalating need for data-driven insights across various sectors highlights the pivotal significance of Data Analytics. For individuals aiming to remain informed in this dynamic landscape, gaining expertise in Data Analytics opens doors to continuous learning and holds the promise of rewarding career opportunities.

In Stockholm, DataMites stands out as a leading institute in the global landscape of Data Analytics Training in Stockholm. Offering a Certified Data Analyst Course in Stockholm, the program caters to beginners and intermediate learners, delivering a career-oriented curriculum. With a focus on Data Analysis, Data Science Foundation, Statistics, Visual Analytics, Data Modeling, and Predictive Modeling, this comprehensive course ensures a strong foundational understanding. Participants in Stockholm also benefit from the included IABAC Certification, elevating their professional standing in the competitive field of Data Analytics. 

Phase 1 - Pre Course Self-Study:

  1. Access to high-quality videos with an easy learning approach.

Phase 2 - 3-Month Duration:

  1. Live training sessions totaling 20 hours per week.
  2. Comprehensive syllabus covering key aspects of Data Analytics.
  3. Engage in hands-on projects for practical experience.
  4. Benefit from expert trainers and mentors guiding the learning process.

Phase 3 - 3-Month Duration:

  1. Specialized project mentoring to enhance practical skills.
  2. Involvement in 5+ capstone projects for real-world application.
  3. Real-time internship opportunities for hands-on industry experience.
  4. Participation in a live client project to apply acquired knowledge.
  5. Attainment of IABAC and Data Analytics Internship Certification for professional recognition.

Certified Data Analyst Courses in Stockholm - Features

Faculty Expertise - Ashok Veda:

  1. Lead instructor Ashok Veda, with over 19 years of profound experience in Data Analytics, delivers top-tier education.
  2. As Founder & CEO at Rubixe™, he exemplifies expertise in both Data Analytics and AI.

Course Curriculum - No-Code Program (Optional Python):

  1. A 6-month program offering 20 hours of learning per week, accumulating 200+ learning hours.
  2. Features a No-Code Program with an optional Python module for a comprehensive learning experience.

Global Certification - IABAC® Certification:

  1. Successful completion results in prestigious IABAC® Certification, globally recognized for professional competence.

Flexible Learning - Online Courses and Self-Study:

  1. Flexibility in learning through online data analytics courses in Stockholm and self-study options.

Real-World Projects and Internship Opportunity:

  1. Engage in hands-on learning with 5+ capstone projects and a live client project, utilizing real-world data.
  2. Avail data analytics internship opportunities in Stockholm for practical industry exposure.

Career Guidance and Job Support:

  1. End-to-end job support, personalized resume, and data analytics interview preparation.
  2. Continuous assistance with job updates and connections for a seamless career transition.

Exclusive Learning Community:

  1. Join DataMites' exclusive learning community, fostering collaboration, networking, and continuous learning.

Affordable Pricing and Scholarships:

  1. Affordable pricing, with Data Analytics course fees in Stockholm ranging from SEK 4392 to SEK 13506.
  2. Scholarships available for eligible candidates, ensuring accessibility to quality education.

The Data Analytics sector in Stockholm is undergoing substantial growth, fueled by the rising demand for data-driven insights across various industries. The city stands out as a focal point for professionals seeking opportunities in the dynamic realm of Data Analytics.

Data Analysts in Sweden are rewarded with a substantial average annual salary of 508,000 SEK, as reported by Salary Explorer. This significant compensation reflects the strategic importance attributed to professionals capable of extracting valuable insights from intricate datasets. The noteworthy earning potential underscores the premium placed on individuals skilled in translating raw data into actionable intelligence, emphasizing the pivotal role of Data Analysts in Stockholm's competitive employment landscape.

For professionals aspiring to thrive in Stockholm's dynamic job market, DataMites emerges as the gateway to unlocking career triumph. Complementing our esteemed Data Analytics Courses in Stockholm, a diverse selection of courses, including Python, Machine Learning, Artificial Intelligence, Data Engineering, Tableau, Data Science, and more, awaits your exploration. pt for DataMites for an educational journey that transforms and embark on a trajectory toward unparalleled career growth in Stockholm.

ABOUT DATAMITES DATA ANALYST COURSE IN STOCKHOLM

Data analytics entails extracting insights from raw data to inform decision-making and optimize processes, utilizing techniques such as statistical analysis, machine learning, and data visualization.

Data analysts typically perform tasks such as data collection and cleansing, statistical analysis, creation of data visualizations, and report generation to extract insights and guide decision-making processes.

Proficiency in data analytics within six months is attainable through focused study, practice, and hands-on projects. However, achieving mastery may necessitate longer-term dedication and practical experience.

Projects provide hands-on experience, enabling learners to apply theoretical concepts to real-world data, fostering critical thinking, problem-solving skills, and reinforcing understanding through practical application.

Crucial skills for data analytics include proficiency in programming, statistical analysis, data visualization, critical thinking, and domain expertise.

Primary roles in data analytics careers include data analyst, data scientist, business intelligence analyst, and data engineer, each specializing in different aspects of data management and analysis.

The future of data analysis looks promising, driven by advancements in artificial intelligence, machine learning, and big data technologies, leading to more sophisticated analytics capabilities and increased automation.

Absolutely, there's a plethora of consulting opportunities within data analytics, offering services in strategy development, implementation, and optimization of data-driven solutions for businesses.

Internships are pivotal for gaining practical experience, exposure to real-world datasets, and the chance to collaborate with professionals, facilitating the application of theoretical knowledge, skill refinement, and networking essential for a flourishing career in data analytics.

Essential tools for mastering data analytics include programming languages such as Python or R, statistical software like Excel or SPSS, data visualization tools such as Tableau or Power BI, and database management systems like SQL.

The data analytics course can indeed present challenges due to its multidisciplinary nature, requiring proficiency in statistics, programming, and critical thinking skills.

Salary Explorer reports that Data Analysts in Sweden command a substantial average annual salary of 508,000 SEK.

DataMites provides outstanding data analytics training in Stockholm, covering statistical methods, machine learning, and data visualization. Through practical projects and expert instructors, DataMites prepares students for successful careers in data analytics.

Data analytics contributes to business expansion by providing actionable insights derived from data analysis. This helps organizations identify growth opportunities, streamline processes, and make informed decisions that foster innovation and competitiveness.

Data analytics intersects with machine learning by employing algorithms and statistical models to analyze data, identify patterns, and make predictions or classifications. This integration enhances decision-making processes and automates tasks based on data-driven insights.

Qualifications required for a data analyst training typically include a bachelor's degree in a related field like computer science, mathematics, statistics, or economics, along with proficiency in programming and statistical analysis.

Predictive analytics is implemented by using historical data to develop models and algorithms that forecast future trends, behaviors, or events. This enables organizations to anticipate outcomes, make proactive decisions, and optimize strategies for better results.

Data analytics is utilized for risk management by analyzing historical data to identify patterns or anomalies indicating potential risks or opportunities. Predictive models are then developed to anticipate and mitigate risks, enabling organizations to implement effective risk mitigation strategies.

While data analytics may involve coding, the level of proficiency required varies based on the role and tasks. Basic coding skills in languages like Python or R are often necessary for data manipulation, analysis, and visualization, but the depth of coding expertise depends on specific job requirements.

Absolutely, there's a significant demand for data analytics jobs across industries due to the increasing volume and complexity of data generated.

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FAQ’S OF DATA ANALYST TRAINING IN STOCKHOLM

Beginners and intermediate learners seeking to venture into data analytics can join DataMites' Certified Data Analyst Training in Stockholm. Covering essential areas like data analysis, statistics, visual analytics, and predictive modeling, the program prepares participants for prosperous careers in the field.

Commence your data analytics journey with DataMites' Certified Data Analyst Course in Stockholm, providing flexible learning options, a practical curriculum, experienced instructors, dedicated lab access, an engaged learning community, and lifelong resource access. Offering limitless project opportunities and job placement aid, DataMites ensures a comprehensive and impactful learning journey.

DataMites' certified data analyst training in Stockholm covers essential tools like Power BI, essential for crafting interactive data dashboards and reports.

DataMites' Certified Data Analyst Course in Stockholm is tailored for advanced analytics and business insights, offering a NO-CODE option for learners to explore analytics without coding prerequisites.

Indeed, DataMites incorporates live projects into its data analyst course in Stockholm. Participants engage in 5+ capstone projects and collaborate on 1 client/live project. These practical initiatives offer firsthand experience in applying data analytics skills to real-world situations, enhancing participants' proficiency and competitiveness in the industry.

The Flexi Pass for DataMites' Certified Data Analyst Course in Stockholm grants participants the flexibility to structure their learning journey. With this option, learners can access course materials and attend sessions at their convenience, enabling them to manage their studies alongside other commitments effectively.

DataMites' Data Analytics Course in Stockholm is priced between SEK 4392 to SEK 13506. This range offers flexibility to learners with diverse budgets, ensuring accessibility to quality education in the field of data analytics. Participants can choose a pricing option that suits their financial circumstances while receiving comprehensive training.

Certainly, DataMites is dedicated to providing support for participants to understand data analytics course topics in Stockholm through experienced educators, interactive resources, mentorship, and a collaborative learning environment.

DataMites' Data Analyst Course in Stockholm extends over 6 months, requiring a weekly commitment of 20 learning hours. With over 200 learning hours in total, participants receive extensive training in data analytics, equipping them for success in the field.

The Certified Data Analyst Training in Stockholm covers areas such as Data Analysis Foundation, Statistics Essentials, Data Analysis Associate, Advanced Data Analytics, Predictive Analytics with Machine Learning, Database Management with SQL and MongoDB, Git Version Control, Big Data Foundation, Python Foundation, and Certified Business Intelligence (BI) Analyst.

Ashok Veda and esteemed mentors lead the Certified Data Analyst Course in Stockholm at DataMites, offering participants invaluable insights and guidance derived from their extensive experience in Data Science and AI at leading companies and esteemed institutes like IIMs.

DataMites utilizes a case study-focused methodology in its Certified Data Analyst Course in Stockholm. Participants engage in analyzing real-world data sets, enhancing their data analysis skills through practical application. This immersive learning approach fosters deeper understanding and equips learners to confidently tackle complex data challenges.

DataMites offers data analytics courses in Stockholm through various learning methods, including online data analytics training in Stockholm and self-paced learning. Participants can attend interactive online sessions or progress through course materials at their own pace, providing flexibility to accommodate individual learning preferences and schedules.

In the event of missing a data analytics session in Stockholm, DataMites provides recorded sessions for flexible viewing. Additionally, supplementary study materials are available to help participants catch up on missed content, ensuring they stay on track with the course curriculum despite any absences.

Payment options for the Certified Data Analytics Course at DataMites in Stockholm include cash, debit card, check, credit card, EMI, PayPal, Visa, Mastercard, American Express, and net banking.

Yes, participants who complete the Certified Data Analyst Course in Stockholm at DataMites receive the prestigious IABAC Certification. This certification validates their proficiency in data analytics, enhancing their professional credibility and opening doors to rewarding career opportunities in data-driven industries.

Absolutely, DataMites' Certified Data Analyst Course holds significant value in Stockholm. It's the most comprehensive non-coding course, providing accessibility to data analytics for individuals without technical backgrounds. With a three-month internship at an AI company, an experience certificate, and the prestigious IABAC Certification, participants gain industry recognition and numerous career opportunities.

Yes, DataMites provides internship opportunities alongside the Certified Data Analyst Course in Stockholm. Learners benefit from exclusive partnerships with renowned Data Science companies, gaining practical, hands-on experience. This internship enables them to apply theoretical knowledge in real-world scenarios, mentored by DataMites experts, fostering professional growth and relevance in the industry.

Participants are required to bring valid photo identification, such as a national ID card or driver's license, to the training sessions. This documentation is essential for receiving the participation certificate and scheduling certification exams, ensuring proper identification and accountability throughout the training program.

DataMites in Stockholm organizes mentoring sessions for data analytics careers to provide personalized guidance and support. These sessions involve one-on-one meetings with experienced mentors who offer tailored advice, insights, and career development strategies to help individuals advance in their data analytics careers.

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: -

  • 1. Job connect
  • 2. Resume Building
  • 3. Mock interview with industry experts
  • 4. Interview questions

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.

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