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 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 objects
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
• Operator’s precedence and associativity
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
• String object basics and inbuilt methods
• List: Object, methods, comprehensions
• Tuple: Object, methods, comprehensions
• Sets: Object, methods, comprehensions
• Dictionary: Object, methods, comprehensions
MODULE 4: PYTHON FUNCTIONS
• Functions basics
• Function Parameter passing
• Iterators
• Generator functions
• Lambda functions
• Map, reduce, filter functions
MODULE 5: PYTHON NUMPY PACKAGE
• NumPy Introduction
• Array – Data Structure
• Core Numpy functions
• Matrix Operations
MODULE 6: PYTHON PANDAS PACKAGE
• Pandas functions
• Data Frame and Series – Data Structure
• Data munging with Pandas
• Imputation and outlier analysis
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: COMPARISION AND CORRELATION ANALYSIS
• Data comparison Introduction
• Concept of Correlation
• Calculating Correlation with Excel
• Comparison vs Correlation
• Performing Comparison Analysis on Data
• Performing correlation Analysis on Data
• Hands-on case study 1: Comparison Analysis
• Hands-on case study 2 Correlation Analysis
MODULE 2: VARIANCE AND FREQUENCY ANALYSIS
• Concept of Variability and Variance
• Data Preparation for Variance Analysis
• Business use cases for Variance and Frequency Analysis
• Performing Variance and Frequency Analysis
• Hands-on case study 1: Variance Analysis
• Hands-on case study 2: 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: Procurement Decision with break even
MODULE 5: PARETO (80/20 RULE) ANALSYSIS
• Pareto rule Introduction
• Preparation Data for Pareto Analysis
• Insights on Optimizing Operations with Pareto Analysis
• Performing Pareto Analysis on Data
• 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
• Hands-on Case Study: Trend Analysis
MODULE 7: DATA ANALYSIS BUSINESS REPORTING
• Management Information System Introduction
• Various Data Reporting formats
• Creating Data Analysis reports as per the requirements
• Presenting the reports
• Hands-on case study: Create Data Analysis Reports
MODULE 1: DATA ANALYTICS FOUNDATION
• Business Analytics Overview
• Application of Business Analytics
• Visual Perspective
• Benefits of Business Analytics
• Challenges
• Classification of Business Analytics
• Data Sources
• Data Reliability and Validity
• Business Analytics Model
MODULE 2: OPTIMIZATION MODELS
• Prescriptive Analytics with Low Uncertainty
• Mathematical Modeling and Decision Modeling
• Break Even Analysis
• Product Pricing with Prescriptive Modeling
• Building an Optimization Model
• Case Study 1 : WonderZon Network Optimization
• Assignment 1 : KERC Inc, Optimum Manufacturing Quantity
MODULE 3: PREDICTIVE ANALYTICS WITH REGRESSION
• Mathematics beyond Linear Regression
• Hands on: Regression Modeling in Excel
• Case Study 2 : Sales Promotion Decision with Regression Analysis
• Assignment 2 : Design Marketing Decision board for QuikMark Inc.
MODULE 4: DECISION MODELING
• Prescriptive Analytics with High Uncertainty
• Comparing Decisions in Uncertain Settings
• Decision Trees for Decision Modeling
• Case Study 3 : Decision modeling of Internet Plans, Monte Carlo Simulation
• Case Study 4 : Kickathlon Sports Retailer Supplier Decision Modeling
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
• How it works: Classification & Sigmoid Curve
• Hands-on Logistics Regression with ML Tool
MODULE 4: ML ALGO: KNN
• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Hands-on KNN with ML Tool
MODULE 5: ML ALGO: K MEANS CLUSTERING
• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Hands-on K Means Clustering with ML Tool
MODULE 6: ML ALGO: DECISION TREE
• Random Forest Ensemble technique
• How it works: Bagging Theory
• 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
• Modeling and Evaluation of SVM in Python
MODULE 8: ARTIFICIAL NEURAL NETWORK (ANN)
• Introduction to ANN
• How It Works: Back prop, Gradient Descent
• Modeling and Evaluation of ANN in Python
MODULE 9: PROJECT: PREDICTIVE ANALYTICS WITH ML
• Project Business requirements
• Data Modeling
• Building Predictive Model with ML Tool
• Evaluation and Deployment
• Project Documentation and Report
MODULE 1: GIT INTRODUCTION
• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture
MODULE 2: GIT REPOSITORY and GitHub
• Git Repo Introduction
• Create New Repo with Init command
• Copying existing repo
• Git user and remote node
• Git Status and rebase
• Review Repo History
• GitHub Cloud Remote Repo
MODULE 3: COMMITS, PULL, FETCH AND PUSH
• Code commits
• Pull, Fetch and conflicts resolution
• Pushing to Remote Repo
MODULE 4: TAGGING, BRANCHING AND MERGING
• Organize code with branches
• Checkout branch
• Merge branches
MODULE 5: UNDOING CHANGES
• Editing Commits
• Commit command Amend flag
• Git reset and revert
MODULE 6: GIT WITH GITHUB AND BITBUCKET
• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers
• Bitbucket Git account
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
• Comments
• import and export dataset
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
• Cross join
• Self join
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
• Hands-on Map Reduce task
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
• Working with Spark SQL Query Language
MODULE 5: MACHINE LEARNING WITH SPARK ML
• Introduction to MLlib Various ML algorithms supported by Mlib
• ML model with Spark ML.
• Linear regression
• logistic regression
• Random forest
MODULE 6: KAFKA and Spark
• Kafka architecture
• Kafka workflow
• Configuring Kafka cluster
• Operations
MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION
• What Is Business Intelligence (BI)?
• What Bi Is The Core Of Business Decisions?
• BI Evolution
• Business Intelligence Vs Business Analytics
• Data Driven Decisions With Bi Tools
• The Crisp-Dm Methodology
MODULE 2: BI WITH TABLEAU: INTRODUCTION
• The Tableau Interface
• Tableau Workbook, Sheets And Dashboards
• Filter Shelf, Rows And Columns
• Dimensions And Measures
• Distributing And Publishing
MODULE 3: TABLEAU: CONNECTING TO DATA SOURCE
• Connecting To Data File , Database Servers
• Managing Fields
• Managing Extracts
• Saving And Publishing Data Sources
• Data Prep With Text And Excel Files
• Join Types With Union
• Cross-Database Joins
• Data Blending
• Connecting To Pdfs
MODULE 4: TABLEAU : BUSINESS INSIGHTS
• Getting Started With Visual Analytics
• Drill Down And Hierarchies
• Sorting & Grouping
• Creating And Working Sets
• Using The Filter Shelf
• Interactive Filters
• Parameters
• The Formatting Pane
• Trend Lines & Reference Lines
• Forecasting
• Clustering
MODULE 5: DASHBOARDS, STORIES AND PAGES
• Dashboards And Stories Introduction
• Building A Dashboard
• Dashboard Objects
• Dashboard Formatting
• Dashboard Interactivity Using Actions
• Story Points
• Animation With Pages
MODULE 6: BI WITH POWER-BI
• Power BI basics
• Basics Visualizations
• Business Insights with Power BI
Projects provide hands-on learning opportunities, enabling learners to apply theoretical knowledge to real-world data sets. This practical engagement fosters critical thinking, problem-solving skills, and reinforces understanding through direct application.
Yes, there is a significant demand for data analytics professionals across various industries, driven by the escalating volume and complexity of data being generated.
Data analytics refers to the process of deriving insights from raw data to support decision-making and enhance processes, employing techniques such as statistical analysis, machine learning, and data visualization.
The future of data analysis appears promising, propelled by advancements in artificial intelligence, machine learning, and big data technologies. These innovations are expected to lead to more sophisticated analytics capabilities and increased automation in data analysis processes.
Key skills for data analytics include proficiency in programming languages, statistical analysis, data visualization, critical thinking, and domain expertise relevant to the field being analyzed.
Primary roles in data analytics careers encompass positions such as data analyst, data scientist, business intelligence analyst, and data engineer, each specializing in distinct aspects of data management and analysis.
Indeed, there are numerous consulting opportunities within Data Analytics, providing services in strategy formulation, implementation, and optimization of data-driven solutions for businesses.
Internships play a pivotal role in learning Data Analytics by offering practical experience, exposure to real-world datasets, and the opportunity to collaborate with professionals, facilitating the application of theoretical knowledge, skill enhancement, and networking essential for a successful career.
According to Salary Explorer, Data Analysts in Sweden earn a significant average yearly salary of 508,000 SEK.
Essential tools for mastering Data Analytics include programming languages like Python or R, statistical software such as Excel or SPSS, data visualization tools like Tableau or Power BI, and database management systems like SQL.
The Data Analytics course can be challenging due to its multidisciplinary nature, requiring proficiency in statistics, programming, and critical thinking skills.
Proficiency in Data Analytics within six months is achievable through focused study, practice, and hands-on projects. However, mastery may require longer-term dedication and practical experience.
Prerequisites for a data analyst training typically include a bachelor's degree in fields like computer science, mathematics, statistics, or economics, along with proficiency in programming and statistical analysis.
Data analysts typically engage in activities such as data collection and cleaning, statistical analysis, creation of data visualizations, and report generation to extract insights and support decision-making processes.
DataMites delivers exceptional data analytics training in Sweden, covering statistical methods, machine learning, and data visualization. With practical projects and expert instructors, DataMites prepares students for successful careers in data analytics.
Machine learning intersects with data analytics by employing algorithms and statistical models to analyze data, detect patterns, and make predictions or classifications. This synergy enhances decision-making processes and automates tasks based on data-driven insights.
Predictive analytics is applied by using historical data to develop models and algorithms that forecast future trends or events. This enables organizations to anticipate outcomes, make proactive decisions, and optimize strategies for better results.
Data analytics aids risk management by analyzing historical data, identifying patterns or anomalies indicating potential risks, and developing predictive models to anticipate and mitigate various risks. This enables organizations to make informed decisions and implement effective risk mitigation strategies.
While data analytics may require coding, the extent varies based on the role and tasks. Basic coding skills in languages like Python or R are often necessary for tasks such as data manipulation and analysis, but proficiency levels vary depending on job requirements.
Data analytics fuels business expansion by delivering actionable insights from data analysis. This helps organizations identify growth opportunities, streamline processes, and make informed decisions, fostering innovation and enhancing competitiveness.
Beginners and intermediate learners with an interest in data analytics qualify for enrollment in DataMites' Certified Data Analyst Training in Sweden. The program covers essential areas like data analysis, statistics, visual analytics, and predictive modeling, setting participants on the path to successful careers in the field.
Embark on your data analytics journey with DataMites' Certified Data Analyst Course in Sweden, offering flexible learning formats, a curriculum designed for real-world applications, experienced instructors, a dedicated practice lab, an engaged learning community, and lifetime access to resources. With opportunities for unlimited projects and placement assistance, DataMites ensures a comprehensive and impactful learning experience.
The Data Analyst Course in Sweden provided by DataMites extends over 6 months, requiring a weekly commitment of 20 learning hours. With over 200 learning hours in total, participants receive thorough training in data analytics to thrive in the industry.
DataMites' certified data analyst training in Sweden includes tools such as Power BI, essential for crafting interactive data dashboards and reports.
DataMites' Certified Data Analyst Course in Sweden is tailored for advanced analytics and business insights, offering a NO-CODE option for learners to explore analytics without coding prerequisites.
The Flexi Pass for the Certified Data Analyst Course in Sweden at DataMites empowers participants to structure their learning experience flexibly. With this option, learners can access course materials and attend sessions at their convenience, enabling effective balance between studies and other commitments.
The pricing for DataMites' Data Analytics Course in Sweden ranges from SEK 4392 to SEK 13506. This diverse pricing structure caters to different budgets, making the course accessible to a wide range of participants. It ensures affordability while providing comprehensive training in data analytics for individuals in Sweden.
Certainly, DataMites is dedicated to supporting participants in grasping data analytics concepts in Sweden. Through experienced educators, interactive resources, personalized mentorship, and a collaborative learning environment, participants receive continuous assistance to ensure comprehension and success in the program.
The curriculum of the Certified Data Analyst Training in Sweden covers areas such as Data Analysis Foundation, Statistics Essentials, Data Analysis Associate, Advanced Data Analytics, Predictive Analytics with Machine Learning, Database Management (SQL and MongoDB), Version Control (Git), Big Data Foundation, Python Foundation, and Certified Business Intelligence (BI) Analyst.
Payment options for the Certified Data Analytics Course at DataMites in Sweden include cash, debit card, check, credit card, EMI, PayPal, Visa, Mastercard, American Express, and net banking.
Ashok Veda and esteemed mentors lead the Certified Data Analyst Course in Sweden at DataMites. With extensive experience in Data Science and AI, instructors offer participants invaluable insights and guidance derived from their real-world experience at leading companies and prestigious institutes such as IIMs.
In its Certified Data Analyst Course in Sweden, DataMites adopts a methodology centered on case studies. Participants actively analyze real-world data sets, refining their data analysis skills through practical application. This immersive learning approach enhances comprehension and equips learners with the capability to tackle complex data challenges confidently.
DataMites offers data analytics courses in Sweden through various learning methods, including online data analytics training in Sweden and self-paced learning. Participants can attend interactive online sessions or progress through course materials independently, granting them flexibility to learn at their own pace and convenience.
In the event of missing a data analytics session in Sweden, DataMites provides recorded sessions for flexible viewing. Additionally, supplementary study materials and resources are available to help participants bridge any knowledge gaps. This ensures that participants remain on track with the course curriculum despite missing a session.
Absolutely, participants who complete the Certified Data Analyst Course in Sweden at DataMites receive the prestigious IABAC Certification. This recognized credential validates their proficiency in data analytics, enhancing their professional credibility and opening doors to rewarding career opportunities in industries valuing data-driven decision-making.
DataMites in Sweden structures its 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 strategies for career development, assisting individuals in advancing their careers in data analytics.
Absolutely, DataMites' Certified Data Analyst Course in Sweden holds significant value. It stands out as the most comprehensive non-coding course, making data analytics accessible even to individuals without technical backgrounds. With features like a three-month internship at an AI company, an experience certificate, and the prestigious IABAC Certification, participants gain industry recognition and ample career opportunities.
Yes, DataMites does offer internships alongside its Certified Data Analyst Course in Sweden. Participants benefit from exclusive partnerships with renowned Data Science companies, gaining valuable hands-on experience. This internship opportunity allows them to apply theoretical knowledge in practical settings, guided by DataMites experts, fostering their professional growth and industry relevance.
Indeed, DataMites incorporates live projects into its data analyst course in Sweden. Participants engage in over 5 capstone projects and collaborate on at least 1 client/live project. These practical initiatives provide firsthand experience in applying data analytics skills to real-world scenarios, enhancing participants' proficiency and competitiveness in the industry.
Participants are expected to bring valid photo identification, such as a national ID card or driver's license, to training sessions. These documents are essential for receiving the participation certificate and scheduling certification exams. They ensure proper identification and accountability throughout the training program.
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.