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Curriculum in Data Science

Data Science Basics
  • Introduction to Data Science
  • Significance of Data Science
  • R Programming basics
    • Python Fundamentals
  • Python Introduction
  • Indentations in Python
  • Python data types and operators
  • Python Functions
    • Data Structures and Data Manipulation
  • Data Structures Overview
  • Identifying the Data Structures
  • Allocating values to the Data Structures
  • Data Manipulation Significance
  • Dplyr Package and performing different data manipulation operations
    • Data visualization
  • Introduction to Data Visualisation
  • Various kinds of graphs, Graphics grammar
  • Ggplot2 package
  • Multivariant analysis by using geom_boxplot
  • Univariant analysis
  • Histogram, barplot, multivariate distribution, and density plot
  • Bar plots for the categorical variables through geop_bar() and the theme() layer
    • Statistics
  • Statistics Importance
  • Statistics classification, Statistical terminology
  • Data types, Probability types, measures of speed, and central tendency
  • Covariance and Correlation, Binary and Normal distribution
  • Data Sampling, Confidence, and Significance levels
  • Hypothesis Test and Parametric testing
    • Introduction to Machine Learning
  • Machine Learning Fundamentals
  • Supervised Learning, Classification in Supervised Learning
  • Linear Regression and mathematical concepts related to linear regression
  • Classification Algorithms, Ensemble Learning techniques
    • Logistic Regression
  • Logistic Regression Introduction
  • Logistic vs Linear Regression, Poisson Regression
  • Bivariate Logistic Regression, math related to logistic regression
  • Multivariate Logistic Regression
  • Building Logistic Models
  • False and true positive rate
  • Real-time applications of Logistic Regression
    • Random Forest and Decision Trees
  • Classification Techniques
  • Decision Tree Induction Algorithm
  • Implementation of Random Forest in R
  • Differences between classification tree and regression tree
  • Naive Bayes, SVM
  • Entropy, Gini Index, Information Gain
    • Unsupervised learning
  • Clustering, K-means clustering, Canopy Clustering, and Hierarchical Clustering
  • Unsupervised learning, Clustering algorithm, K-means clustering algorithm
  • K-means theoretical concepts, k-means process flow, and K-means implementation
  • Implementing Historical Clustering in R
  • PCA(Principal Component Analysis) Implementation in R
    • Denial-of-Service
  • DoS/DDoS Concepts
  • Botnets
  • DoS/DDoS Attack Techniques
  • DDoS Case Study
  • DoS/DDoS Countermeasures
    • Natural Language Processing
  • Natural language processing and Text mining basics
  • Significance and use-cases of text mining
  • NPL working with text mining, Language Toolkit(NLTK)
  • Text Mining: pre-processing, text-classification and cleaning
    • Mathematics for Data Science
  • Numpy Basics
  • Numpy Mathematical Functions
  • Probability Basics and Notation
  • Correlation and Regression
  • Joint Probabilities
  • Bayes Theorem
  • Conditional Probability, sum rule, and product rule
    • Scientific Computing through Scipy
  • Scipy Introduction and characteristics
  • Integrate, Cluster, Signal, Fftpack, and Bayes Theorem
    • Python Integration with Spark
  • Pyspark basics
  • Uses and Need of pyspark
  • Pyspark installation
  • Advantages of pyspark over MapReduce
  • Pyspark applications
    • Deep Learning and Artificial Intelligence
  • Machine Learning effect on Artificial Intelligence
  • Deep Learning Basics, Working of Deep Learning
  • Regression and Classification in the Supervised Learning
  • Association and Clustering in unsupervised learning
  • Basics of Artificial Intelligence and Neural Networks
  • Supervised Learning in Neural Networks, multi-layer network
  • Deep Neural Networks, Convolutional Neural Networks
  • Reinforcement Learning
  • Recurrent Neural Networks, Deep learning graphics processing unit
  • Deep Learning Applications, Time series modeling
    • Keras and TensorFlow API
  • Tensorflow Basics and Tensorflow open-source libraries
  • Deep Learning Models and Tensor Processing Unit(TPU)
  • Graph Visualisation, keras
  • Keras neural-network
  • Define and Composing multi-complex output models through Keras
  • Batch normalization, Functional and Sequential composition
  • Implementing Keras with tensorboard
  • Implementing neural networks through TensorFlow API
    • Restricted Boltzmann Machine and Autoencoders
  • Basics of Autoencoders and rbm
  • Implementing RBM for the deep neural networks
  • Autoencoders features and applications
    • Big Data Hadoop and Spark
  • Big Data and Hadoop Basics
  • Hadoop Architecture, HDFS
  • MapReduce Framework and Pig
  • Hive and HBase
  • Basics of Scala and Functional Programming
  • Kafka basics, Kafka Architecture
  • Kafka cluster and Integrating Kafka with Flume
  • Introduction to Spark
  • Spark RDD Operations, writing spark programs
  • Spark Transformations, Spark streaming introduction
  • Spark streaming Architecture, Spark Streaming Features
  • Structured streaming Architecture, Dstreams, and Spark Graphx
    • Tableau
  • Data Visualisation Basics
  • Data Visualisation Applications
  • Tableau Installation and Interface
  • Tableau Data Types, Data Preparation
  • Tableau Architecture
  • Getting Started with Tableau
  • Creating sets, Metadata and Data Blending
  • Arranging visual and data analytics
  • Mapping, Expressions, and Calculations
  • Parameters and Tableau prep
  • Stories, Dashboards, and Filters
  • Graphs, charts
  • Integrating Tableau with Hadoop and R
    • MongoDB
  • MongoDB and NoSQL Basics
  • MongoDB Installation
  • Significance of NoSQL
  • CRUD Operations
  • Data Modeling and Management
  • Data Indexing and Administration
  • Data Aggregation Schema
  • MongoDB Security
  • Collaborating with Unstructured Data
    • SAS Basics
  • SAS Enterprise Guide
  • SAS functions and Operators
  • SAS Data Sets compilation and creation
  • SAS Procedures
  • SAS Graphs
  • SAS Macros
  • PROC SQL
  • Advance SAS
    • MS Excel
  • Entering Data
  • Logical Functions
  • Conditional Formatting
  • Validation, Excel formulas
  • Data sorting, Data Filtering, Pivot Tables
  • Creating charts, Charting techniques
  • File and Data security in excel
  • VBA macros, VBA IF condition, and VBA loops
  • VBA IF condition, For loop
  • VBA Debugging and Messaging

    • Curriculum in Data Science

    Data science is preferred by more than 55% of developers. In the tech industry, data science is the most well-liked and in-demand programming language.

    • Introduction to Data Science
    • Significance of Data Science
    • R Programming basics
    • Python Introduction
    • Indentations in Python
    • Python data types and operators
    • Python Functions
    • Data Structures Overview
    • Identifying the Data Structures
    • Allocating values to the Data Structures
    • Data Manipulation Significance
    • Dplyr Package and performing different data manipulation operations
    • Introduction to Data Visualisation
    • Various kinds of graphs, Graphics grammar
    • Ggplot2 package
    • Multivariant analysis by using geom_boxplot
    • Univariant analysis
    • Histogram, barplot, multivariate distribution, and density plot
    • Bar plots for the categorical variables through geop_bar() and the theme() layer
    • Statistics Importance
    • Statistics classification, Statistical terminology
    • Data types, Probability types, measures of speed, and central tendency
    • Covariance and Correlation, Binary and Normal distribution
    • Data Sampling, Confidence, and Significance levels
    • Hypothesis Test and Parametric testing
    • Machine Learning Fundamentals
    • Supervised Learning, Classification in Supervised Learning
    • Linear Regression and mathematical concepts related to linear regression
    • Classification Algorithms, Ensemble Learning techniques
    • Logistic Regression Introduction
    • Logistic vs Linear Regression, Poisson Regression
    • Bivariate Logistic Regression, math related to logistic regression
    • Multivariate Logistic Regression
    • Building Logistic Models
    • False and true positive rate
    • Real-time applications of Logistic Regression
    • Classification Techniques
    • Decision Tree Induction Algorithm
    • Implementation of Random Forest in R
    • Differences between classification tree and regression tree
    • Naive Bayes, SVM
    • Entropy, Gini Index, Information Gain
    • Clustering, K-means clustering, Canopy Clustering, and Hierarchical Clustering
    • Unsupervised learning, Clustering algorithm, K-means clustering algorithm
    • K-means theoretical concepts, k-means process flow, and K-means implementation
    • Implementing Historical Clustering in R
    • PCA(Principal Component Analysis) Implementation in R
    • DoS/DDoS Concepts
    • Botnets
    • DoS/DDoS Attack Techniques
    • DDoS Case Study
    • DoS/DDoS Countermeasures
    • Natural language processing and Text mining basics
    • Significance and use-cases of text mining
    • NPL working with text mining, Language Toolkit(NLTK)
    • Text Mining: pre-processing, text-classification and cleaning
    • Numpy Basics
    • Numpy Mathematical Functions
    • Probability Basics and Notation
    • Correlation and Regression
    • Joint Probabilities
    • Bayes Theorem
    • Conditional Probability, sum rule, and product rule
    • Scipy Introduction and characteristics
    • Integrate, Cluster, Signal, Fftpack, and Bayes Theorem
    • Pyspark basics
    • Uses and Need of pyspark
    • Pyspark installation
    • Advantages of pyspark over MapReduce
    • Pyspark applications
    • Machine Learning effect on Artificial Intelligence
    • Deep Learning Basics, Working of Deep Learning
    • Regression and Classification in the Supervised Learning
    • Association and Clustering in unsupervised learning
    • Basics of Artificial Intelligence and Neural Networks
    • Supervised Learning in Neural Networks, multi-layer network
    • Deep Neural Networks, Convolutional Neural Networks
    • Reinforcement Learning
    • Recurrent Neural Networks, Deep learning graphics processing unit
    • Deep Learning Applications, Time series modeling
    • Tensorflow Basics and Tensorflow open-source libraries
    • Deep Learning Models and Tensor Processing Unit(TPU)
    • Graph Visualisation, keras
    • Keras neural-network
    • Define and Composing multi-complex output models through Keras
    • Batch normalization, Functional and Sequential composition
    • Implementing Keras with tensorboard
    • Implementing neural networks through TensorFlow API
    • Basics of Autoencoders and rbm
    • Implementing RBM for the deep neural networks
    • Autoencoders features and applications
    • Big Data and Hadoop Basics
    • Hadoop Architecture, HDFS
    • MapReduce Framework and Pig
    • Hive and HBase
    • Basics of Scala and Functional Programming
    • Kafka basics, Kafka Architecture
    • Kafka cluster and Integrating Kafka with Flume
    • Introduction to Spark
    • Spark RDD Operations, writing spark programs
    • Spark Transformations, Spark streaming introduction
    • Spark streaming Architecture, Spark Streaming Features
    • Structured streaming Architecture, Dstreams, and Spark Graphx
    • Data Visualisation Basics
    • Data Visualisation Applications
    • Tableau Installation and Interface
    • Tableau Data Types, Data Preparation
    • Tableau Architecture
    • Getting Started with Tableau
    • Creating sets, Metadata and Data Blending
    • Arranging visual and data analytics
    • Mapping, Expressions, and Calculations
    • Parameters and Tableau prep
    • Stories, Dashboards, and Filters
    • Graphs, charts
    • Integrating Tableau with Hadoop and R
    • MongoDB and NoSQL Basics
    • MongoDB Installation
    • Significance of NoSQL
    • CRUD Operations
    • Data Modeling and Management
    • Data Indexing and Administration
    • Data Aggregation Schema
    • MongoDB Security
    • Collaborating with Unstructured Data
    • SAS Enterprise Guide
    • SAS functions and Operators
    • SAS Data Sets compilation and creation
    • SAS Procedures
    • SAS Graphs
    • SAS Macros
    • PROC SQL
    • Advance SAS
    • Entering Data
    • Logical Functions
    • Conditional Formatting
    • Validation, Excel formulas
    • Data sorting, Data Filtering, Pivot Tables
    • Creating charts, Charting techniques
    • File and Data security in excel
    • VBA macros, VBA IF condition, and VBA loops
    • VBA IF condition, For loop
    • VBA Debugging and Messaging

    Data Science Training Projects

    Initiatives that will help you improve your data science abilities while being entertaining and useful.

     

    Exploratory Data Analysis

    Analyze a dataset, clean the data, and create visualizations to gain insights.

     

    Iris Flower Classification

    Build a machine learning model to classify iris flowers into different species based on their features.

     

    Predicting House Prices

    Create a regression model to predict house prices based on features like square footage, bedrooms, and location.

     

    Image Captioning

    Develop a model that generates descriptive captions for images using computer vision and NLP.

     

    Topic Modeling

    Use techniques like Latent Dirichlet Allocation (LDA) to extract topics from a collection of text documents.

     

    Credit Scoring Model

    Create a model to assess credit risk and predict creditworthiness of loan applicants.

     

    Multi-Modal Emotion Recognition

    Create a model that recognizes emotions from multi-modal data, combining text, audio, and visual information.

     

    Autonomous Vehicle Object Detection

    Develop an object detection model for autonomous vehicles, using datasets like Waymo or Udacity's self-driving car dataset.

     

    Transformer-based Language Model

    Fine-tune transformer-based models like GPT-3 or BERT for specific natural language processing tasks.

    Best Industry Tools Covered

    Key Highlights

    Practical Training

    Global Certifications

    Flexible Timing

    Trainer Support

    Study Material

    Placement Support

    Mock Interviews

    Resume Building

    Our Instructor

    Learn from professionals who are currently employed and licensed.

    Instructor
    Priya

    Experience : 3 Years

    Priya is a machine learning expert with a focus on Python and NLP, making her a valuable resource for learners interested in AI and advanced text analytics.

    Specialized in : Machine Learning, Python Programming, Natural Language Processing (NLP), Deep Learning

    Instructor
    David

    Experience : 3 Years

    David is a big data specialist, providing insights into distributed computing frameworks and NoSQL databases, ideal for those working on large-scale data.

    Specialized in : Big Data Analytics, Hadoop, Spark, and NoSQL Databases

    Instructor
    Sophia

    Experience : 4 Years

    Sophia is an expert in data engineering, helping students master the intricacies of data pipelines, ETL processes, and cloud-based data platforms.

    Specialized on : Data Engineering, ETL Processes, and Cloud Data Platforms

    Data Science Training Overview

    Data science is an interdisciplinary field that amalgamates elements of mathematics, statistics, computer science, and specialized knowledge domains to extract wisdom and discernments from data. It encompasses tasks like gathering, refining, and scrutinizing extensive datasets to unveil significant patterns, trends, and interrelationships. Data scientists employ a diverse set of methodologies, encompassing machine learning, data mining, and statistical analysis, to make anticipations and guide data-influenced decision-making. These revelations find extensive utility, ranging from enhancing operational efficiency and fine-tuning marketing strategies to applications in healthcare diagnostics and environmental forecasting. Essentially, data science empowers both organizations and individuals to leverage the potential of data for resolving intricate issues and steering innovative progress.

    Additional Information

    Data Science's Future Scope:

    • The need for data scientists and professionals adept in data analysis and interpretation is poised for growth. Organizations are progressively relying on data-centric decision-making as a means to gain a competitive advantage, thereby driving the demand for expertise in data science.
    • The applicability of data science transcends industry boundaries, encompassing sectors like healthcare, finance, retail, marketing, agriculture, and more. As technology evolves, data science will unearth fresh utility in a multitude of domains.
    • The amalgamation of AI and machine learning into the realm of data science is on a trajectory of expansion. This convergence will facilitate more intricate predictive modeling and the automation of data-oriented tasks.
    • Given the meteoric rise in data generation, grappling with and gleaning insights from big data will remain a pivotal challenge and opportunity for data scientists.
    • As the scope of data collection and analysis widens, ethical considerations and the protection of data privacy will persist as paramount concerns. Data scientists must grapple with these issues and champion responsible data practices.
    • Data scientists will be expected to wield a more comprehensive skill set, encompassing expertise in specific domains, effective communication abilities, and the capacity to collaborate seamlessly across diverse teams. These competencies will be instrumental for proficient problem-solving.

    What Kind of Programming Skills Will You Learn at the Data Science Training?

    During a data science training program, you will develop a diverse range of programming skills that are vital for the tasks of data extraction, analysis, and interpretation. These encompass a strong command of programming languages such as Python and R, which are extensively employed for data manipulation, statistical assessments, and machine learning applications. You will gain proficiency in coding for data cleansing and preprocessing, conducting advanced statistical evaluations, crafting data visualizations, and constructing machine learning models. Furthermore, you may also acquire expertise in utilizing critical data science libraries and frameworks like NumPy, Pandas, scikit-learn, and TensorFlow. This training will extend to encompass programming competencies for working with big data tools, managing databases, as well as techniques for web scraping and API integration for data acquisition. These programming proficiencies are the cornerstone of a data scientist's ability to effectively explore data and derive valuable insights from it.

    Data Science Course Career Opportunities:

    The successful completion of a data science course can unveil a wide array of career opportunities spanning diverse sectors. Here are some potential career paths and roles to consider:

    • Data scientists are equipped with the expertise to dissect and interpret complex datasets, providing valuable insights by constructing machine learning models and applying statistical methods to address business challenges.
    • Machine learning engineers focus on crafting and implementing machine learning models within production environments. They work in close collaboration with data scientists to put predictive algorithms into practical use.
    • Data analysts are entrusted with the task of meticulously examining data to uncover patterns, generate reports, and formulate data-driven recommendations. They often make use of business intelligence tools and techniques for data visualization.
    • Business intelligence analysts are dedicated to leveraging data for informed decision-making within organizations. They specialize in designing and creating data dashboards and reports that facilitate decision support.
    • Data engineers shoulder the responsibility of overseeing and optimizing data pipelines, databases, and data infrastructure. Their role is pivotal in ensuring the efficient collection and storage of data for subsequent analysis.
    • Big data engineers specialize in working with extensive datasets and technologies like Hadoop and Spark. They are adept at designing and maintaining systems tailored to the management of substantial data volumes.

    What Does the Growth of Data Science Look Like in the Coming Years?

    In the forthcoming years, the expansion of data science promises to be both dynamic and revolutionary. Fueled by the exponential surge in data generation and the ever-expanding influence of data-centric decision-making across various sectors, the demand for data scientists and professionals skilled in data analysis is anticipated to maintain its upward trajectory. The integration of artificial intelligence and machine learning is set to have a significant impact, facilitating the development of more advanced predictive models and the automation of tasks. Furthermore, the scope of data science applications is likely to broaden even further, extending its reach beyond the realms of business and technology, encompassing domains like healthcare, environmental sciences, and public policy. In this evolving landscape, ethical considerations, data privacy, and the promotion of responsible data practices will remain at the forefront. To remain pertinent and effective, data scientists must adapt to emerging technologies, tools, and interdisciplinary knowledge, ensuring that data science continues to serve as a cornerstone for innovation and informed decision-making in the foreseeable future.

    Future Prospects and Techniques in Data Science

    The future of data science is filled with promising opportunities, propelled by technological advancements and the escalating significance of data-centric decision-making. Here are some forthcoming possibilities and emerging methodologies in the realm of data science:

    • The fusion of artificial intelligence (AI) and machine learning will persist as a prevailing trend. Enhanced algorithms and models will facilitate more precise predictions, support natural language processing, empower computer vision, and bolster applications like reinforcement learning.
    • Deep learning techniques, encompassing deep neural networks and convolutional neural networks, will witness increased prominence in addressing intricate challenges, spanning domains such as image recognition, speech processing, and autonomous systems.
    • As AI systems grow in complexity, there will be a growing emphasis on crafting models that are transparent and interpretable, fostering trust and comprehension of the decision-making process.
    • AutoML tools will streamline the machine learning process, making it more accessible to a wider spectrum of professionals and diminishing barriers to entry.
    • This approach, which allows machine learning models to be trained on decentralized data sources while preserving privacy and security, will gain increasing relevance in scenarios that involve data sharing.
    • Data analysis and machine learning will shift closer to data sources, reducing latency and enabling real-time processing at the edge of networks. This will be particularly valuable in applications such as the Internet of Things (IoT) and autonomous vehicles.
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    Devops Training Objectives

    Data Science relies on a variety of programming languages, with Python and R being among the most prominent choices. Python is particularly popular in the Data Science community due to its extensive libraries and readability, making it well-suited for tasks such as data analysis, machine learning, and data visualization. R, on the other hand, is designed specifically for statistics and data analysis, boasting a rich ecosystem of packages for data manipulation and visualization.

    The difficulty of Data Science varies depending on a person's background, experience, and the specific aspects of data science they are working on. Data Science can involve complex mathematical and statistical concepts, programming, and domain-specific knowledge. While some aspects of Data Science can be challenging, it is also accessible to individuals who are willing to learn and have a strong foundation in relevant areas.

    Data Science can be a high-paying job in many regions and industries. Experienced data scientists with specialized skills in machine learning, deep learning, and data analysis are often well-compensated. However, salaries can vary significantly based on factors such as location, experience, the specific job role, and the demand for data scientists in a particular area.

  • Students looking to enter the field of Data Science.
  • Professionals in related fields (e.g., mathematics, statistics, computer science) who want to transition into data science.
  • Individuals already working in data-related roles seeking to enhance their skills and advance their careers.
  • Anyone interested in harnessing data for problem-solving and decision-making.
  • Statistics and probability
  • Data preprocessing and cleaning
  • Data visualization
  • Machine learning algorithms
  • Regression analysis
  • Classification techniques
  • Clustering and dimensionality reduction

    • The prerequisites and credentials needed to take a Data Science course also vary by program and institution. Generally, individuals interested in Data Science should have a strong foundation in mathematics and statistics. A bachelor's degree in a related field, such as computer science, mathematics, or statistics, is often beneficial but not always required.

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    Industry Statistics

    Jobs / Month

    248

    Avg. Salary

    ₹ 12,55,200

    Job Roles

    Data Scientist

    ML Engineer

    Data Analyst

    Data Engineer

    Data Science Certification

    Certificate
    GET A SAMPLE CERTIFICATE
  • Data Analysis
  • Machine Learning
  • Data Visualization
  • Statistics
  • Programming
  • Data Wrangling

    • Obtaining a Data Science certification does not guarantee employment, but it can significantly enhance your employment prospects. It demonstrates your commitment to the field and your mastery of key skills, making you a more attractive candidate to potential employers.

    Yes, you can pursue multiple Data Science course certifications. This can help you build a broader skill set and increase your knowledge in specific areas of Data Science, making you more versatile in the job market.

    Prerequisites for Data Science certification programs vary, but typically, you'll need a strong foundation in mathematics, statistics, and programming. Some programs may require a bachelor's degree in a related field, while others admit individuals with diverse educational backgrounds, especially if they demonstrate proficiency in relevant areas.

    The importance of having a Data Science certification lies in its ability to validate your skills and knowledge to employers. It can open doors to job opportunities, provide a competitive advantage, and help you stand out in a field where competition is high.

  • Data Scientist
  • Machine Learning Engineer
  • Data Analyst
  • Business Analyst
  • AI Researcher
  • Skill Validation
  • Competitive Edge
  • Enhanced Earning Potential
  • Career Opportunities
  • Professional Growth
  • Confidence and Credibility
  • Certified Data Scientist (CDS) by DASCA (Data Science Council of America)
  • Microsoft Certified: Data Scientist Associate
  • IBM Data Science Professional Certificate
  • Google Data Analytics Professional Certificate
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    The Preferred Partner for 100+ Organizations' Hiring

    Learn from the certified and real time working professionals.

    • Over 100 firms that are looking for top talent for their open positions have come to rely on ACTE as their go-to partner.

    • Businesses have confidence in our ability to match them with the best individuals because of our considerable expertise and proven track record of success.

    • In this section, we'll examine the primary elements influencing this trust and examine how our constant commitment to excellence regularly results in remarkable results for our clients.

    Corporate Clients

    Data Science Course Duration and Fees

    Level Course Duration Fees Structure
    Basic 1 - 1.5 Months ₹7,000 - ₹9,000
    Advanced 1.5 - 2 Months ₹7,000 - ₹10,000

    Job Opportunities in Data Science

    Over 45% of developers say that data science is their preferred field. In the tech sector, Data Science is the most extensively used and sought-after programming language.

    Salary In Data Science
    Reach Our Placement Officer

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    AI Infrastructure EngineerAI Product ManagerAI in Healthcare SpecialistAI in Cybersecurity AnalystAI in Agriculture Data Scientist

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    Student Testimonials

    Anania Fresher

    Data Analyst | Bangalore

     Got 10 LPA Package

    More Details :I enrolled in Acte Training Institute's Data Analyst program as a fresher, and it was a game-changer for me. The course content was comprehensive, and the instructors were highly knowledgeable. The hands-on projects provided practical experience, and I'm now working as a Data Analyst at a reputed company. Acte truly paved the way for my career in data analysis.

    Ashwini Fresher

    Data Scientist | Chennai

     Got 10 LPA Package

    More Details : Acte's Data Scientist training program is top-notch. As a fresher, I had no prior experience in this field, but the instructors made the complex concepts easy to understand. The career guidance and support were exceptional, and I am now a Data Scientist at a leading tech firm. Thanks, Acte!

    Saravanan Fresher

    Machine Learning Engineer | Hyderabad

     Got 8 LPA Package

    More Details : I made a successful transition from being a fresher to a Machine Learning Engineer with Acte's training. The hands-on approach and real-world projects were invaluable. Acte's trainers are industry experts, and their guidance helped me secure a role as a Machine Learning Engineer at a tech startup.

    100% Placement

    7000+ Placed Student

    600+ Hiring Partners

    5.5 LPA Average Salary

    Recently Placed Students

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    Data Science Training FAQ's

    Enhance Your Coding Abilities: Comprehensive Data Science Training for Every Level!

    Domain knowledge plays a crucial role in data science projects. Understanding the specific industry or domain in which you are working allows data scientists to ask relevant questions, interpret results, and make informed decisions. Domain knowledge helps in data preprocessing, feature engineering, and the selection of appropriate models.

  • Python: NumPy, pandas, scikit-learn, TensorFlow, PyTorch, Matplotlib, Seaborn, Plotly.
  • R: dplyr, ggplot2, caret, xgboost, randomForest, Shiny.
  • Jupyter Notebook: A web-based interactive environment for data analysis and visualization that supports multiple programming languages, including Python and R.

    • Data science is an interdisciplinary field that uses scientific methods, algorithms, processes, and systems to extract knowledge and insights from structured and unstructured data. It combines elements of statistics, machine learning, data analysis, data engineering, and domain knowledge to solve complex problems and make data-driven decisions.

    Key skills required to become a data scientist include proficiency in programming (Python and/or R), statistical analysis, machine learning, data preprocessing, data visualization, domain knowledge, problem-solving, and effective communication. Additionally, data scientists often need to be well-versed in data manipulation, big data technologies, and the ethical considerations of working with data.

    Data science differs from traditional statistics in several ways. While statistics primarily focuses on the analysis of sample data to make inferences about populations, data science encompasses a broader set of tasks, including data collection, cleaning, feature engineering, and model deployment.

  • Problem Definition
  • Data Collection
  • Data Preprocessing
  • Exploratory Data Analysis
  • Feature Engineering
  • Model Building
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  • Check the reputation of the institution or organization offering the course. Look for universities, established online education platforms, or well-known companies.
  • Read reviews and testimonials from past students to gauge their experiences and the course's quality.
  • Investigate the instructors' qualifications. Experienced instructors with relevant industry or academic backgrounds can be a sign of a high-quality course.
  • Assess the curriculum and syllabus. Ensure it covers a wide range of data science topics, including statistics, machine learning, data visualization, and data ethics.
  • Look for courses that provide hands-on experience through projects or practical exercises.
  • Confirm that the course offers good student support, like forums, discussion boards, or access to instructors for questions and clarifications.
  • Set a study schedule and stick to it to maintain consistency.
  • Actively participate in discussion forums or communities to engage with peers and instructors.
  • Work on real-world data science projects to apply what you've learned.
  • Don't hesitate to ask questions and seek help when needed.
  • Practice coding regularly, especially in languages like Python and R.
  • Take thorough notes to reinforce your understanding of key concepts.
  • Consider the time commitment and budget you can allocate.
  • Research different courses and programs, comparing their curricula, instructors, and available resources.
  • Seek recommendations from professionals in the field.
  • Look for courses that align with your preferred learning style, whether it's video lectures, interactive exercises, or a combination.

    • Online data science learning is the process of acquiring knowledge and skills in data science, machine learning, and analytics through internet-based courses, programs, and resources. It differs from traditional, in-person education in several key ways. Online learning offers unparalleled flexibility, allowing individuals to set their own schedules and study from any location with an internet connection.

  • Course Modules
  • Video Lectures
  • Readings and Materials
  • Assignments and Projects
  • Discussion Forums
  • Instructor Support
  • Certificate of Completion
  • Professional Certifications
  • Specializations or MicroMasters
  • Data Science Degrees
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    Data science plays a critical role in helping organizations extract actionable insights and value from data. Its functions include data collection, data analysis, predictive modeling, and data-driven decision-making. Data scientists help companies leverage data to improve operations, optimize processes, enhance customer experiences, and make informed strategic choices.

  • Improved decision-making based on data-driven insights.
  • Enhanced customer understanding, leading to better customer satisfaction and engagement.
  • Cost reduction through process optimization and efficiency improvements.
  • Identifying growth opportunities, market trends, and competitive advantages.
  • Predictive analytics to prevent issues and plan for the future.
  • Personalized recommendations and targeted marketing for higher conversion rates.
  • Predictive maintenance to reduce equipment downtime.
  • Customer churn prediction and retention strategies.
  • Supply chain optimization and inventory management.
  • Fraud detection and cybersecurity.
  • Employee performance and attrition prediction.
  • Quality control and defect detection.
  • Market segmentation and product recommendations.
  • Netflix's recommendation system, which uses data science to suggest content to users.
  • Amazon's supply chain optimization for efficient inventory management.
  • Airbnb's dynamic pricing models to maximize revenue.
  • Uber's route optimization to minimize travel time and costs.
  • Facebook's content recommendation algorithms for personalized user experiences.
  • Predictive maintenance in manufacturing, reducing downtime and maintenance costs.
  • E-commerce websites' fraud detection algorithms to prevent payment fraud.
  • Define your organization's data science goals and objectives.
  • Identify the right data science leaders and managers.
  • Recruit data scientists, analysts, and engineers with the necessary skills.
  • Invest in data infrastructure and tools.
  • Encourage a culture of data-driven decision-making.
  • Provide ongoing training and support for the team.
  • Data Scientists
  • Data Analysts
  • Data Engineers
  • Machine Learning Engineers
  • Domain Experts
  • Data Science Managers
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