CERTIFIED DATA ANALYST CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ANALYTICS LEAD MENTORS

DATA ANALYST COURSE FEES IN CAPE TOWN

Live Virtual

Instructor Led Live Online

ZAR 29,010
ZAR 16,862

  • IABAC® & JAINx® 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

ZAR 14,510
ZAR 9,659

  • Self Learning + Live Mentoring
  • IABAC® & JAINx® 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 CAPE TOWN

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

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SYLLABUS OF DATA ANALYST CERTIFICATION IN CAPE TOWN

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 

  • 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
  • Simple Random Sampling
  • Stratified Random Sampling
  • Cluster Random Sampling
  • Systematic Random Sampling
  • Biased Random Sampling Methods
  • 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
  • Z Value / Standard Value
  • Empherical Rule  and Outliers
  • Central Limit Theorem
  • Normality Testing
  • Skewness & Kurtosis
  • Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance

MODULE 4 : HYPOTHESIS TESTING 

  • Hypothesis Testing Introduction
  • P- Value, Confidence Interval
  • Parametric Hypothesis Testing Methods
  • Hypothesis Testing Errors : Type I And Type Ii
  • One Sample T-test
  • Two Sample Independent T-test
  • Two Sample Relation T-test
  • One Way Anova Test

MODULE 5 : CORRELATION AND REGRESSION

  • Correlation Introduction
  • Direct/Positive Correlation
  • Indirect/Negative Correlation
  • Regression
  • Choosing Right Method
     

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

OFFERED DATA ANALYST COURSES IN CAPE TOWN

DATA ANALYST TRAINING COURSE REVIEWS

ABOUT DATAMITES DATA ANALYST TRAINING IN CAPE TOWN

According to a Precedence Research report, the global market size is estimated to grow by USD 346.33 Billion by 2030. The demand for data analysts in Capetown has been further accelerated by the increasing adoption of advanced technologies like artificial intelligence and machine learning, and the consequent rise of digital transformation in various sectors.

DataMites is a renowned provider of data analytics course in Capetown, offering a comprehensive Certified Data Analyst program that caters to beginners and intermediate learners in data analytics. With a track record of training 50,000 students globally, DataMites provides students with valuable internship and job placement opportunities upon completion of the program. The course is designed to equip students with a strong foundation in data analytics, covering essential topics such as data science basics, statistics, visual analytics, data modeling, and predictive modeling. One unique feature of the course is that it does not require any coding knowledge. The curriculum is tailored to prepare students for a successful career in data analytics by enabling them to identify patterns in unstructured data and make informed business decisions. DataMites offers students access to a specialized syllabus, mock tests, and high-quality study materials, as well as job placement and internship programs that align with industry requirements.

The Certified Data Analyst Training program in Capetown offered by DataMites has a duration of six months, comprising two months of live online instruction, two months of practical projects, and two months of internship experience where students get to apply their acquired skills in real-world scenarios. By enrolling in this program with an internship, students can improve their chances of securing entry-level analytics jobs. DataMites' instructors are highly qualified and have the expertise to extract meaningful insights from raw data, guiding students through the entire data analysis process, including data cleaning, visualization creation, and more. The program's credibility and recognition in the industry are further enhanced by its approval by IABAC, a global organization. The Certified Data Analyst Course offered by DataMites in Capetown is a comprehensive program that equips students with practical knowledge and hands-on experience, preparing them for a successful career in data analytics.

Capetown's data analytics industry is poised for a bright future, thanks to increasing investments in technology infrastructure, a rising demand for data-driven decision-making, and a thriving startup ecosystem. According to a Glassdoor report, data analysts in Capetown can earn an average salary of R 29,132 per year. One of the major drivers of this industry is the growth of the digital economy, which has led to an explosion of data generated by digital devices and the internet. This data can be harnessed by businesses and organizations to gain insights and improve their operations. Furthermore, the emergence of artificial intelligence and machine learning is offering new avenues to analyze data and make predictions. To acquire knowledge and online training in the Certified Data Analyst Course in Capetown, consider enrolling with DataMites.

Along with the data analyst courses, DataMites also provides python training, deep learning, data engineer, data analytics, r programming, mlops, artificial intelligence, machine learning and data science courses in Capetown.

ABOUT DATA ANALYST COURSE IN CAPE TOWN

Data analytics is the process of examining and interpreting large and complex data sets to extract insights and valuable information that can be used to make informed decisions and improve business outcomes. It involves using statistical and computational methods to analyze and visualize data.

Data analytics primarily involves analyzing and interpreting data to extract insights and inform decision-making, while data science involves a broader range of skills and techniques, including computer programming, machine learning, and statistical modeling, to solve complex problems and develop predictive models. Data science also involves more emphasis on data preparation and cleaning, as well as experimental design and hypothesis testing.

Yes, anyone can pursue a career in data analytics with the right skills and training. A background in math, statistics, or computer science can be helpful, but there are also many online courses, boot camps, and degree programs available to help individuals develop the necessary skills and knowledge for a career in data analytics.

Important skills for data analytics include proficiency in programming languages such as Python and SQL, knowledge of statistical and data analysis techniques, the ability to manipulate and clean data, and effective communication and visualization skills to convey insights to stakeholders. Additionally, critical thinking, problem-solving, and attention to detail are important traits for success in data analytics.

Some of the most commonly used tools and techniques in data analytics include programming languages like Python and R, data visualization tools like Tableau and Power BI, databases like MySQL and MongoDB, and machine learning libraries like sci-kit-learn and TensorFlow. Techniques such as regression analysis, clustering, and decision trees are also frequently used in data analytics.

The cost of Data Analytics training can vary depending on the institute and the level of training desired. In Capetown, the fees for Data Analytics training can range from 8986.51 ZAR to 20219.65 ZAR, with different institutes offering different rates.

For those seeking a career in the analytics industry, DataMites is an excellent choice as a training provider. Their instructors are highly knowledgeable and have industry experience, and their course curriculum is well-designed. DataMites also offers practical training through projects and internships to give students real-world experience.

Data analytics has a wide range of job opportunities across various industries, including finance, healthcare, e-commerce, and marketing. Some popular job roles in data analytics include data analyst, business analyst, data scientist, data engineer, and data architect, among others.

Data analytics can be utilized in various fields, including healthcare, finance, marketing, e-commerce, sports, social media, and many others. It can be applied to optimize business operations, improve customer experience, develop targeted marketing strategies, and make data-driven decisions in a range of industries.

According to a Glassdoor report, data analysts in Capetown can earn an average salary of R 29,132 per year

FAQ’S OF DATA ANALYST COURSE IN CAPE TOWN

DataMites offers excellent data analyst certification training in Capetown that provides tangible evidence of your expertise in data analytics. This training equips you with the skills necessary to help companies interpret data and make informed decisions, which can open up job opportunities with well-known multinational corporations. A certification from DataMites demonstrates your ability to perform specific job roles according to professional standards, making it more valuable than a simple data analytics certificate.

The Certified Data Analyst Course in Capetown by DataMites is an excellent choice for those interested in a career in data analytics or data science, as it is a no-coding course that requires no prior programming experience. The training program is well-organized and designed to provide a comprehensive understanding of the subject matter, making it an ideal choice for beginners. If you are intrigued by analytics, signing up for this course is a great way to explore the field further.

DataMites, a global institute for data science, has received approval from the International Association of Business Analytics Certifications (IABAC). They have trained over 50,000 candidates in data science and analytics, using a three-phase learning process and real-world projects and case studies to provide the best possible training. Completing the course earns an internationally recognized certification, the IABAC Data Analytics Certification and students also have the opportunity to work as an intern for Rubixe, a leading AI startup.

The cost of DataMites' certified data analytics training can vary depending on the type of training you select. Typically, in Capetown, the cost of a certified data analytics course can range from 14,205 ZAR to 26,390 ZAR depending on the mode of training.

DataMites offers six months of data analytics training, which includes 20 hours of instruction each week.

If you're considering a career as a data analyst, completing the DataMites Certified Data Analyst Training is an excellent option. Our training program is designed to provide you with a comprehensive curriculum that will equip you with the skills, certifications, and confidence to start your data analyst journey from the ground up. Rest assured that our program will give you the necessary knowledge and expertise to succeed in this field.

DataMites offers a Flexi-Pass for the Certified Data Analytics Training, allowing candidates to attend any relevant sessions within a three-month timeframe for clarification or revision purposes. This means that candidates have the flexibility to choose sessions that align with their specific needs and clear any doubts or questions they may have during the training period.

We offer multiple payment options for your convenience, including cash, debit card, check, credit card (Visa, Mastercard, American Express), PayPal, and net banking. You can choose the payment method that best suits your preference and make your payment securely and easily.

Yes, Our accreditation from IABAC® guarantees international recognition of your relevant skills and abilities. You can be confident that your training has met the required standards, and your accomplishments will be acknowledged globally.

DataMites offers a top-tier data analytics program, the Certified Data Analyst curriculum, which is accredited by the internationally recognized IABAC. Completing this course will earn you credentials from the IABAC, providing you with valuable recognition in the industry. The best way to launch a career in data analytics is by obtaining the DataMites Certified Data Analyst certification.

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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