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 the field that involves extracting valuable insights and knowledge from large volumes of structured and unstructured data. It employs various techniques, algorithms, and systems to analyze, interpret, and present data.
Data Science operates by collecting, cleaning, and analyzing data to derive meaningful patterns and trends. It often involves the use of statistical models, machine learning algorithms, and data visualization techniques to make informed decisions.
Applications of Data Science include predictive analytics, fraud detection, recommendation systems, sentiment analysis, and optimizing business processes across various industries.
Key components of a Data Science pipeline include data collection, data cleaning, exploratory data analysis (EDA), feature engineering, model training, model evaluation, and deployment.
Common programming languages in Data Science include Python and R. They are popular for their extensive libraries and frameworks that facilitate data manipulation, analysis, and machine learning.
Machine learning is integral to Data Science, as it enables systems to learn patterns from data and make predictions or decisions without explicit programming. It enhances the ability to extract valuable insights from complex datasets.
Big Data is closely linked to Data Science as it involves handling and analyzing massive datasets that traditional data processing tools may struggle with. Data Science techniques and algorithms are often applied to extract meaningful information from Big Data.
Data Science is applied in industries such as healthcare, finance, marketing, and manufacturing to optimize operations, improve decision-making, and enhance overall business performance.
While Data Science encompasses a broader range of activities, including data cleaning, exploration, and visualization, machine learning specifically focuses on developing algorithms that enable systems to learn patterns and make predictions.
Individuals from diverse backgrounds, including IT professionals, statisticians, analysts, and business professionals, are eligible to pursue Data Science certification courses. A basic understanding of statistics and programming is beneficial for learning Data Science.
The data science job market in Madagascar in 2024 is growing, with increasing demand for skilled professionals.
Recognized as a top-notch choice for data science training, the Certified Data Scientist Course in Madagascar delves into crucial subjects like machine learning and data analysis.
Data science internships are valuable in Madagascar, providing practical experience and enhancing employability.
Salaries for data science roles in Madagascar can range from MGA 20,000,000 per year according to a Glassdoor report.
Yes, a fresher can do a data science course and secure a job in Madagascar, as companies are open to hiring skilled beginners.
A postgraduate degree is not mandatory for enrolling in data science training courses in Madagascar; many accept candidates with relevant undergraduate backgrounds.
Madagascar businesses leverage data science for growth by improving decision-making, optimizing operations, and enhancing customer experiences.
In finance, data science is applied to risk management, fraud detection, and predictive analytics.
Data science contributes to e-commerce by powering recommendation systems, personalized marketing, and demand forecasting.
In cybersecurity, data science detects anomalies, identifies patterns, and enhances threat detection and prevention measures.
In manufacturing and supply chain management, data science optimizes production processes, predicts demand, and improves logistics efficiency.
The Datamites™ Certified Data Scientist course offers a well-rounded curriculum covering programming, statistics, machine learning, and business knowledge. With a focus on Python as the primary language and the inclusion of R for those familiar, the course provides a solid foundation in data science. Successful completion leads to an IABAC™ certificate, preparing individuals to excel as proficient data science professionals.
While a background in statistics can be advantageous, it's not always a prerequisite for a data science career in Madagascar. Proficiency in pertinent tools, programming languages, and practical problem-solving skills are frequently given greater priority.
Novices in Madagascar seeking foundational training in data science have a range of options to consider, including courses like Certified Data Scientist, Data Science Foundation, and Diploma in Data Science.
Indeed, in Madagascar, DataMites offers a diverse range of courses designed to boost the skills of professionals. These include Statistics for Data Science, Data Science with R Programming, Python for Data Science, Data Science Associate, as well as specialized certifications in Operations, Marketing, HR, and Finance
The data science course in Madagascar has a duration of 8 months.
The career mentoring sessions at DataMites are conducted in an interactive format, providing personalized guidance on resume construction, interview preparation, and career strategies. These sessions offer valuable insights and tactics to enrich the professional journeys of participants in the field of data science
Upon successful completion of DataMites' Data Science Training in Madagacar, participants are awarded the esteemed IABAC Certification. This globally recognized certification serves as proof of their proficiency in data science concepts and practical applications. Functioning as a valuable credential, it validates their expertise and boosts their credibility in the field of data science.
To succeed in data science, establish a solid groundwork in mathematics, statistics, and programming. Cultivate strong analytical skills, achieve proficiency in languages such as Python or R, and acquire hands-on experience with extensive datasets and essential tools like Hadoop or SQL databases.
The data science training fee in Madagascar ranges from MGA 2,161,136 to MGA 5,972,239.
Indeed, DataMites offers a Data Scientist Course in Madagascar that includes hands-on learning with more than 10 capstone projects and a dedicated client/live project. This practical experience enhances participants' skills by providing real-world applications and exposure that is relevant to the industry.
We are dedicated to providing instructors with certifications, decades of extensive industry experience, and a demonstrated mastery of the subject matter.
DataMites provides adaptable learning options, including Live Online sessions and self-study, designed to suit your preferences.
With the FLEXI-PASS option in DataMites' Certified Data Scientist Course, participants have the flexibility to join multiple batches. This allows them to revisit topics, clarify doubts, and enhance their understanding of the course content across various sessions for a comprehensive learning experience.
Certainly, DataMites issues a Certificate of Completion for the Data Science Course. Upon successfully finishing the course, participants can request the certificate via the online portal. This certification validates their proficiency in data science, thereby enhancing their credibility in the job market.
Certainly. A valid Photo ID Proof, such as a National ID card or Driving License, is required to obtain a Participation Certificate and schedule the certification exam as necessary.
If participants miss a session in the DataMites Certified Data Scientist Course in Madagascar, they typically have the choice to access recorded sessions or participate in support sessions. This ensures learners can catch up on missed content, address any doubts, and stay on track with the course curriculum.
Certainly, prospective participants at DataMites can attend a demo class before paying for the Certified Data Scientist Course in Madagascar. This allows individuals to evaluate the teaching style, course content, and overall structure, empowering them to make an informed decision about enrollment.
DataMites sets itself apart by integrating internships into its certified data scientist course in Madagascar, creating a unique learning experience that blends theoretical knowledge with practical industry exposure. The additional benefit of obtaining a data science certification from an AI company enhances skills and increases job opportunities in the continually evolving field of data science.
Upon successfully finishing the Data Science training, you will receive an internationally recognized IABAC® certification. This certification validates your expertise in the field and enhances your employability on a global scale.
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.