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
Key positions in data analytics encompass roles like Data Analyst, Business Intelligence Analyst, Data Scientist, Database Administrator, and Machine Learning Engineer. Each role entails specific responsibilities within the broader field of data analytics.
Data analytics involves inspecting and interpreting raw data to uncover meaningful insights, patterns, and trends. It utilizes diverse statistical and computational techniques to convert data into valuable information for decision-making.
Proficiency in programming languages (such as Python or R), statistical analysis, data visualization, database management, and critical thinking are essential for data analytics. Additionally, strong communication skills are vital for effectively communicating findings to non-technical stakeholders.
The future of data analysis appears promising, driven by a growing reliance on data-driven decision-making across industries. Anticipated advancements in artificial intelligence, machine learning, and big data technologies are poised to augment the capabilities of data analytics, presenting more opportunities for professionals in the field.
Typically, a bachelor's degree in a related field like statistics, mathematics, computer science, or business is the minimum requirement for a data analyst course. However, some courses may consider candidates with relevant work experience or equivalent certifications.
To excel in data analytics, it's essential to acquaint yourself with tools like Python or R for programming, SQL for database management, and data visualization tools such as Tableau or Power BI.
A data analyst is tasked with gathering, processing, and analyzing data to aid organizations in making informed decisions. They apply statistical methods, programming languages, and data visualization tools to decipher intricate datasets and generate actionable insights.
Pursuing data analytics can be demanding due to its multidisciplinary nature, requiring proficiency in statistics, programming, and business acumen. However, these challenges contribute to the development of a comprehensive skill set.
While achieving complete mastery in data analytics within six months is ambitious, individuals can gain a solid foundation and practical skills through intensive study, hands-on projects, and focused learning.
Internships are crucial in data analytics as they provide real-world exposure, enabling learners to apply theoretical knowledge to practical scenarios. This hands-on experience enhances problem-solving skills, teamwork, and industry relevance.
According to Salary Explorer, the average salary for a Data Analyst in Algeria is 1,980,000 DZD. This figure reflects the compensation received by professionals in the field and provides insights into the earning potential for individuals pursuing a career as a Data Analyst in Algeria.
DataMites is recognized for offering high-quality data analytics courses in Algeria. Their programs provide comprehensive training, covering essential tools, techniques, and real-world applications. With a focus on hands-on learning and industry relevance, DataMites ensures participants gain the skills needed to excel in the dynamic field of data analytics.
Projects in data analytics provide hands-on experience, reinforcing theoretical concepts. They enhance problem-solving skills, boost confidence, and allow learners to apply analytics techniques to real-world scenarios, preparing them for professional challenges.
Yes, Python is often considered essential for data analysts due to its versatility in data manipulation, analysis, and visualization, making it a valuable tool in the field.
Yes, data analytics often involves coding. Proficiency in programming languages like Python or R is beneficial for tasks such as data cleaning, statistical analysis, and creating algorithms. However, the required level of coding expertise may vary depending on the specific role and industry.
Yes, there are bootcamps available in Algeria that offer intensive and focused training in data analytics. These programs are designed to impart practical skills in a short duration, catering to individuals seeking a fast-track learning experience.
Data science involves broader tasks such as developing algorithms and predictive models, whereas data analytics focuses on interpreting and analyzing historical data to derive insights for decision-making.
The data analytics job market in Algeria is witnessing growth, with an increasing demand for professionals skilled in big data analytics, machine learning, and business intelligence. Industries such as finance, e-commerce, and healthcare are actively recruiting.
Data analytics plays a pivotal role in enhancing healthcare outcomes in Algeria. It aids in optimizing operations, creating personalized treatment plans, and conducting predictive analysis for disease prevention. Ultimately, it improves the efficiency and effectiveness of healthcare services.
Indeed, data analytics is challenging, requiring proficiency in statistics, programming, and business acumen. The complexity stems from the task of extracting meaningful insights from extensive and diverse datasets.
In DataMites' certified data analyst training in Algeria, participants delve into a broad toolset, including Advanced Excel, MySQL, MongoDB, Git, GitHub, Atlassian BitBucket, Hadoop, Apache Pyspark, Anaconda, Google Collab, Numpy, Pandas, Tableau, and Power BI. This comprehensive range empowers learners with essential skills across diverse facets of data analysis and visualization.
DataMites stands out for the Certified Data Analyst Course in Algeria, ensuring an unmatched learning experience. With a curriculum geared towards real-world applications, a flexible learning structure, esteemed instructors, and an exclusive practice lab, participants engage in a dynamic learning community. The program provides lifelong access, allowing continuous skill enhancement, coupled with numerous opportunities for hands-on projects. Moreover, with dedicated placement assistance, DataMites propels learners towards a successful career in data analytics.
Enrollment in DataMites' Certified Data Analyst Training in Algeria is accessible for beginners and intermediate learners in data analytics. This career-focused program strategically emphasizes the development of a solid foundation in data analysis, data science, statistics, visual analytics, data modeling, and predictive modeling. Geared towards imparting essential skills, it stands out as an excellent choice for those looking to establish or advance their careers in the dynamic field of data analytics.
Setting itself apart, DataMites' Certified Data Analyst Course in Algeria features a specialized Course Bundle focusing on advanced analytics and business insights. Uniquely designed as a No-Code Program, it enables data analytics professionals and managers to engage in advanced analytics without prior programming expertise. With the option to include Python, the course stays dynamically updated to meet industry demands, delivering a structured and efficient learning journey for lean learning.
The fee for DataMites' Data Analytics Course in Algeria varies from DZD 57,754 to DZD 177,591. This flexible pricing structure accommodates diverse budgets, allowing participants to access the comprehensive data analytics training offered by DataMites.
Yes, DataMites in Algeria prioritizes supporting learners in understanding data analytics course topics. This commitment enhances the learning journey, fostering a conducive and comprehensive educational environment.
DataMites' Data Analyst Training in Algeria unfolds over 6 months. With a weekly commitment of 20 hours, the program totals more than 200 learning hours. This carefully structured duration ensures participants acquire in-depth knowledge of key concepts and skills in data analytics, creating a well-paced and immersive learning environment.
In the Certified Data Analyst Course in Algeria, participants delve into key topics, including Data Analysis Foundation, Statistics Essentials, Data Analysis Associate, Advanced Data Analytics, Predictive Analytics with Machine Learning, Database: SQL and MongoDB, Version Control with Git, Big Data Foundation, Python Foundation, and the Certified Business Intelligence (BI) Analyst module. This diverse curriculum facilitates a well-rounded grasp of essential concepts for a thriving career in data analytics.
Ashol Veda, a respected Data Science coach and AI expert, leads the Certified Data Analyst Course at DataMites in Algeria. The team comprises elite mentors and faculty members with real-world experience from leading companies and esteemed institutions like IIMs, ensuring participants receive high-quality mentorship and guidance throughout their educational journey.
With DataMites' Flexi Pass for the Data Analytics Course in Algeria, participants can opt for batches that suit their schedule, granting flexibility in training. This adaptable feature allows learners to synchronize the course with their availability, ensuring optimal convenience and accessibility.
After concluding the Certified Data Analyst Course at DataMites in Algeria, participants are awarded the prestigious IABAC Certification. This widely recognized credential attests to their proficiency in data analytics, strengthening their standing in the industry. Graduates can confidently exhibit this accomplishment, signaling their preparedness for impactful roles.
Payment for DataMites' Certified Data Analytics Course in Algeria is simplified with varied options, such as cash, debit card, credit card (Visa, Mastercard, American Express), check, EMI, PayPal, and net banking. This versatility offers participants convenient choices, streamlining the process of course enrollment and payment.
In DataMites' Certified Data Analyst Course in Algeria, a result-oriented approach is employed. It integrates hands-on sessions, real-world case studies, and industry projects. This ensures participants grasp theoretical concepts and gain practical skills, effectively preparing them for the dynamic field of data analytics.
Flexibility defines DataMites' data analytics courses in Algeria, offering either Online Data Analytics Training in Algeria or Self-Paced Training. Participants have the freedom to choose the mode that resonates with their learning style and availability. Whether opting for instructor-led online sessions or self-paced learning, both avenues deliver a thorough and adaptable educational experience, catered to individual requirements.
For DataMites' data analytics training sessions in Algeria, participants are required to bring a valid photo ID, such as a national ID card or driver's license. This is necessary to acquire the participation certificate and schedule any relevant certification exams.
In Algeria, DataMites structures its data analytics career mentoring sessions with a focus on personalized guidance. Accomplished mentors bring valuable insights into industry trends, aid in crafting impactful resumes, and provide expert guidance for interview preparation. These interactive sessions are tailored to individual career goals, ensuring participants receive customized advice for a prosperous journey.
Yes, the Certified Data Analyst Course offered by DataMites is incredibly valuable in Algeria. It stands out as the most comprehensive non-coding course, enabling individuals from non-technical backgrounds to pursue a career in data analytics. The program includes a unique 3-month internship in an AI company, an experience certificate, and expert-led training, culminating in the prestigious IABAC Certification.
Indeed, DataMites in Algeria incorporates live projects into the data analyst course, showcasing 5+ Capstone Projects and 1 Client/Live Project. This hands-on exposure is crucial, enabling participants to apply their skills in real-world scenarios, elevating practical proficiency and industry preparedness.
Yes, in Algeria, DataMites offers a valuable internship alongside the Certified Data Analyst Course, thanks to exclusive partnerships with leading Data Science Companies. Learners benefit from applying their acquired knowledge in developing real-world data models, adding substantial value to businesses. Expert guidance from DataMites ensures a meaningful and practical internship experience.
Should you miss a data analytics session in Algeria, DataMites offers recorded sessions. Participants can catch up at their convenience, ensuring continuous learning with minimal impact on occasional absence.
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.