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 SCIENCE COURSE INTRODUCTION
MODULE 2: DATA SCIENCE ESSENTIALS
MODULE 3: DATA SCIENCE DEMO
MODULE 4: ANALYTICS CLASSIFICATION
MODULE 5: DATA SCIENCE AND RELATED FIELDS
MODULE 6: DATA SCIENCE ROLES & WORKFLOW
MODULE 7: MACHINE LEARNING INTRODUCTION
MODULE 8: DATA SCIENCE INDUSTRY APPLICATIONS
MODULE 1: PYTHON BASICS
MODULE 2: PYTHON CONTROL STATEMENTS
MODULE 3: PYTHON DATA STRUCTURES
MODULE 4: PYTHON FUNCTIONS
MODULE 5: PYTHON NUMPY PACKAGE
MODULE 6: PYTHON PANDASPACKAGE
MODULE 1: OVERVIEW OF STATISTICS
MODULE 2: HARNESSING DATA
MODULE 3: EXPLORATORY DATA ANALYSIS
MODULE 4: HYPOTHESIS TESTING
MODULE 5: CORRELATION AND REGRESSION
MODULE 1: MACHINE LEARNING INTRODUCTION
MODULE 2: PYTHON NUMPY & PANDAS PACKAGE
MODULE 3: VISUALIZATION WITH PYTHON
MODULE 4: ML ALGO: LINEAR REGRESSION
MODULE 5: ML ALGO: KNN
MODULE 6: ML ALGO: LOGISTIC REGRESSION
MODULE 7: PRINCIPLE COMPONENT ANALYSIS (PCA)
MODULE 8: ML ALGO: K MEANS CLUSTERING
MODULE 1: MACHINE LEARNING INTRODUCTION
MODULE 2: ML ALGO: LINEAR REGRESSSION
MODULE 3: ML ALGO: LOGISTIC REGRESSION
MODULE 4: ML ALGO: KNN
MODULE 5: ML ALGO: K MEANS CLUSTERING
MODULE 6: PRINCIPLE COMPONENT ANALYSIS (PCA)
MODULE 7: ML ALGO: DECISION TREE
MODULE 8 : ML ALGO: NAÏVE BAYES
MODULE 9: GRADIENT BOOSTING, XGBOOST
MODULE 10: ML ALGO: SUPPORT VECTOR MACHINE (SVM)
MODULE 11: ARTIFICIAL NEURAL NETWORK (ANN)
MODULE 12: ADVANCED ML CONCEPTS
MODULE 1: TIME SERIES FORECASTING - ARIMA
MODULE 2: FEATURE ENGINEERING
MODULE 3: SENTIMENT ANALYSIS
MODULE 4: REGULAR EXPRESSIONS WITH PYTHON
MODULE 5: ML MODEL DEPLOYMENT WITH FLASK
MODULE 6: ADVANCED DATA ANALYSIS WITH MS EXCEL
MODULE 7: AWS CLOUD FOR DATA SCIENCE
MODULE 8: AZURE FOR DATA SCIENCE
MODULE 1: DATABASE INTRODUCTION
MODULE 2: SQL BASICS
MODULE 3: DATA TYPES AND CONSTRAINTS
MODULE 4: DATABASES AND TABLES (MySQL)
MODULE 5: SQL JOINS
MODULE 6: SQL COMMANDS AND CLAUSES
MODULE 7 : DOCUMENT DB/NO-SQL DB
MODULE 1: GIT INTRODUCTION
MODULE 2: GIT REPOSITORY and GitHub
MODULE 3: COMMITS, PULL, FETCH AND PUSH
MODULE 4: TAGGING, BRANCHING AND MERGING
MODULE 5: UNDOING CHANGES
MODULE 6: GIT WITH GITHUB AND BITBUCKET
MODULE 1: BIG DATA INTRODUCTION
MODULE 2 : HDFS AND MAP REDUCE
MODULE 3: PYSPARK FOUNDATION
MODULE 4: SPARK SQL and HADOOP HIVE
MODULE 5 : MACHINE LEARNING WITH SPARK ML
MODULE 6: KAFKA and Spark
MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION
MODULE 2: BI WITH TABLEAU: INTRODUCTION
MODULE 3 : TABLEAU: CONNECTING TO DATA SOURCE
MODULE 4: TABLEAU : BUSINESS INSIGHTS
MODULE 5: DASHBOARDS, STORIES AND PAGES
MODULE 6: BI WITH POWER-BI
Data Science is a multidisciplinary field that utilizes scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data.
The Data Science process involves collecting, cleaning, and analyzing data to gain valuable insights, often using statistical techniques and machine learning algorithms, and then presenting the findings to inform decision-making.
Data Science finds applications in various fields, including finance, healthcare, marketing, and social media, aiding tasks like predictive modeling, pattern recognition, and anomaly detection.
Data collection, data cleaning, exploratory data analysis, feature engineering, modeling, evaluation, and deployment are essential components in a typical Data Science pipeline.
Big Data involves handling massive datasets, and Data Science often leverages Big Data technologies to process and analyze these vast amounts of information efficiently.
Data Science enhances e-commerce through personalized recommendations, demand forecasting, and fraud detection, optimizing user experiences and increasing business efficiency.
Data Science plays a crucial role in cybersecurity by identifying patterns in network traffic, detecting anomalies, and predicting potential security threats, thereby strengthening digital defences.
Industries like healthcare use Data Science for patient diagnosis, finance for risk management, and manufacturing for process optimization, showcasing its versatile applications.
While Data Science is a broader field encompassing data analysis, machine learning is a subset focusing specifically on algorithms that enable computers to learn from data and make predictions.
While Data Science is a broader field encompassing data analysis, machine learning is a subset focusing specifically on algorithms that enable computers to learn from data and make predictions.
Create a data science portfolio by showcasing projects, using platforms like GitHub, and highlighting skills such as coding, data analysis, and visualization.
Yes, you can switch from a non-coding background to data science; and focus on learning programming languages like Python, statistics, and machine learning.
A background in mathematics, statistics, computer science, or a related field is commonly required for a career in data science.
Essential skills for a Data Scientist include proficiency in programming languages (e.g., Python, R), statistical analysis, machine learning, data manipulation, and effective communication.
Start a data science career in Antananarivo by acquiring relevant skills, networking, and joining local data science communities.
The data science job market in Antananarivo in 2024 depends on demand; check job portals and networks to gauge the current situation.
The Certified Data Scientist Course in Antananarivo is widely acknowledged as an excellent option for data science training, covering essential topics such as machine learning and data analysis.
Data science internships are valuable in Antananarivo for gaining practical experience, building a network, and enhancing employability.
The average annual salary for data scientists in Antananarivo is reported to be around MGA 2,000,000 according to information from Glassdoor.
Yes, a fresher can do a data science course and secure a job in Antananarivo by building a strong portfolio and actively applying to entry-level positions.
The Datamites™ Certified Data Scientist course provides a comprehensive curriculum covering programming, statistics, machine learning, and business knowledge. It focuses on Python as the primary language, includes R for those familiar, and leads to an IABAC™ certificate, preparing individuals for a successful career in data science.
While a background in statistics can be advantageous, it's not always a prerequisite for a data science career in Antananarivo. Proficiency in relevant tools, programming languages, and practical problem-solving skills are often prioritized.
DataMites offers a range of certifications in Antananarivo, including Diploma in Data Science, Certified Data Scientist, Data Science for Managers, Data Science Associate, Statistics for Data Science, Python for Data Science, and specialized courses in Operations, Marketing, HR, Finance, among others.
Beginners in Antananarivo can explore foundational training options such as Certified Data Scientist, Data Science Foundation, and Diploma in Data Science.
Yes, DataMites in Antananarivo offers specialized courses for professionals, including Statistics for Data Science, Data Science with R Programming, Python for Data Science, and certifications in Operations, Marketing, HR, and Finance.
The data science course in Antananarivo has a duration of 8 months.
The career mentoring sessions at DataMites follow an interactive format, providing personalized guidance on resume building, interview preparation, and career strategies to enhance participants' professional journeys in data science.
Upon successful completion, participants receive the IABAC Certification, globally recognized as evidence of competence in data science concepts and practical applications.
To succeed in data science, establish a solid foundation in mathematics, statistics, and programming. Develop strong analytical skills, proficiency in Python or R, and hands-on experience with tools like Hadoop or SQL databases.
Advantages include flexibility, accessibility, a comprehensive curriculum aligned with industry requirements, industry-relevant content, experienced instructors, and interactive learning environments.
The data science training fee in Antananarivo ranges from MGA 2,161,136 to MGA 5,972,239.
Yes, DataMites provides a Data Scientist Course in Antananarivo with hands-on learning through capstone projects and a dedicated client/live project for practical industry exposure.
Instructors are selected based on certifications, extensive industry experience, and mastery of the subject matter.
DataMites offers adaptable learning options, including Live Online sessions and self-study, designed to suit individual preferences.
The FLEXI-PASS option in DataMites' Certified Data Scientist Course allows participants to join multiple batches for a comprehensive learning experience, revisiting topics and enhancing understanding.
Yes, participants receive a Certificate of Completion, validating their expertise in data science.
A valid Photo ID Proof, such as a National ID card or Driving License, is required for obtaining a Participation Certificate and scheduling the certification exam.
Participants can access recorded sessions or participate in support sessions to catch up on missed content and address doubts.
Yes, prospective participants can attend a demo class before enrollment to evaluate the teaching style, course content, and overall structure.
Yes, DataMites integrates internships into its certified data scientist course in Antananarivo, providing practical industry exposure.
Yes, participants receive an internationally recognized IABAC® certification upon successful completion, enhancing employability globally.
The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -
The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.
No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.