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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

    Interesting and practical assignments to hone your data science abilities.

     

    Weather Data Analysis

    Analyze historical weather data to find trends, temperature changes, or weather anomalies.

     

    Customer Segmentation

    Segment customers based on their behavior, demographics, and spending habits.

     

    Time Series Forecasting

    Forecast future values in a time series, such as stock prices, using techniques like ARIMA or LSTM.

     

    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.

     

    Image Segmentation

    Segment images into different objects or regions, often used in medical imaging or autonomous vehicles.

     

    Anomaly Detection in Network Traffic

    Detect network anomalies and cybersecurity threats using advanced machine learning algorithms.

     

    Reinforcement Learning for Robotics

    Train robots to perform complex tasks or navigate environments using reinforcement learning.

     

    Multi-Label Classification

    Solve multi-label classification problems where each data point can belong to multiple classes.

    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 a multidisciplinary field encompassing the gathering, examination, and interpretation of extensive and intricate data, with the aim of extracting valuable insights and facilitating data-informed decision-making. Drawing from a blend of methodologies from statistics, computer science, mathematics, and domain-specific knowledge, it tackles a broad spectrum of challenges, spanning from forecasting future trends to streamlining business operations. Data scientists deploy a diverse range of tools and algorithms to process and manipulate data, unveil underlying patterns, and construct predictive models. These models find application across diverse domains like finance, healthcare, marketing, and more, contributing to improved decision-making, increased operational efficiency, and the stimulation of innovation. In today's context, data science has evolved into a fundamental component of contemporary enterprises and research, offering organizations the means to transform data into a valuable resource driving growth and competitive advantage.

    Additional Information

    Updated and Specialized Data Science Components:

    The field of data science has undergone a transformation, incorporating a variety of specialized elements tailored to tackle distinctive challenges and opportunities. Some of these advanced and specialized data science components encompass:

    • Big Data Analytics: In response to the abundance of extensive and intricate datasets, big data analytics involves techniques and technologies designed to effectively manage, process, and analyze colossal data volumes.
    • Machine Learning and Deep Learning: These subdomains concentrate on developing algorithms and models that empower computers to acquire knowledge from data and make predictions or decisions, frequently employed in applications such as image recognition, natural language processing, and recommendation systems.
    • Natural Language Processing (NLP): NLP revolves around the creation of algorithms and models that equip computers to grasp, interpret, and generate human language, with applications spanning chatbots, sentiment analysis, and language translation.
    • Computer Vision: This specialized facet pertains to enabling machines to interpret and make sense of visual data, facilitating applications like facial recognition, object detection, and autonomous vehicles.
    • Reinforcement Learning: This distinct branch of machine learning is centered on training algorithms to make sequences of decisions, a concept extensively employed in the realms of autonomous robotics and gaming.
    • Time Series Analysis: Components related to time series data delve into recognizing temporal data patterns and trends, frequently employed in endeavors like forecasting, stock market analysis, and climate modeling.

    Data Science's Future Scope:

    The future prospects of data science are notably bright, characterized by a set of significant trends and opportunities that are shaping its path forward:

    • AI's seamless integration with data science is poised to advance, fostering more sophisticated and self-directed decision-making systems across diverse industries.
    • The accessibility of tools and platforms for automating machine learning procedures (AutoML) is expected to rise, thus diminishing the entry barriers for newcomers and organizations entering the data science realm.
    • Data science's growing synergy with other domains, such as healthcare, finance, and environmental science, will lead to pioneering and influential applications in these areas.
    • The prominence of ethical data practices will increase, with an emphasis on fairness, transparency, and accountability in the realm of data-driven decision-making.
    • As apprehensions regarding data security and privacy intensify, data science will adapt to confront these challenges, incorporating robust measures for safeguarding data and ensuring compliance.
    • With the surge in Internet of Things (IoT) devices, data science's pivotal role in analyzing data at the edge, in proximity to the data source, will enable real-time insights and actions.

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

    A data science training program typically encompasses a diverse set of programming skills and tools that are essential for data manipulation and analysis. Here are some of the primary programming competencies you're likely to acquire during data science training:

    • Python Proficiency: Python stands as the predominant programming language in data science. You will develop the ability to write Python code for data manipulation, analysis, machine learning model development, and the creation of data visualizations.
    • Familiarity with R: R is another widely-used programming language for statistical analysis and data science. Some data science programs may incorporate R into their curriculum, equipping you with the skills needed for data manipulation and statistical analysis.
    • SQL Expertise: SQL (Structured Query Language) is crucial for interacting with relational databases. You'll gain proficiency in writing SQL queries to extract, transform, and analyze data stored in databases.
    • Mastery of Data Manipulation Libraries: You will become skilled in using libraries such as Pandas for Python and data.table for R, which facilitate data cleaning, transformation, and manipulation tasks.
    • Data Visualization Techniques: You'll learn how to create effective data visualizations by leveraging libraries like Matplotlib, Seaborn, ggplot2, and Plotly to convey your analytical findings.
    • Machine Learning Libraries: You'll acquire knowledge in machine learning libraries like Scikit-Learn (Python) or caret (R) to construct and train machine learning models for tasks such as predictive analytics and classification.

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

      The forthcoming years are poised to witness significant and transformative expansion within the field of data science. As we progress deeper into the digital era, the significance of data-informed decision-making is on the ascent across diverse industries. Data science is anticipated to occupy a central role in this ongoing evolution, as businesses increasingly acknowledge the inherent value of data for securing a competitive advantage. The proliferation of IoT devices, the continuous growth of e-commerce, and the incessant generation of massive datasets are set to drive a mounting demand for data scientists capable of harnessing the data's potential. Furthermore, ongoing developments in machine learning and artificial intelligence are likely to enhance the sophistication and accessibility of data analysis and predictive capabilities. Looking ahead, data science is expected to expand its influence into previously untapped domains, encompassing healthcare, finance, and environmental sciences, thereby revolutionizing the way we approach intricate challenges and make well-informed choices. In summary, the trajectory of data science in the forthcoming years is poised to be distinguished by innovation, broadened application, and a substantial influence on various facets of our daily lives and the global economy.

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    Data Science Training Objectives

  • Grasp fundamental data science concepts and principles.
  • Attain proficiency in programming languages such as Python and R for the purpose of data analysis.
  • Cultivate expertise in data manipulation, cleansing, and preprocessing.
  • Acquire proficiency in statistical analysis and the application of machine learning techniques.
  • Develop the ability to interpret data visualizations effectively.
  • Apply data science methodologies to real-world problem-solving.

    • The future prospects for Data Science are exceedingly promising, given the escalating volume of data generated by both businesses and individuals. Data science is poised to remain in high demand, with applications spanning diverse industries, including healthcare, finance, marketing, and technology. Anticipated advancements in artificial intelligence and machine learning are expected to propel data science to even greater significance, making it a pivotal discipline for addressing intricate challenges, facilitating data-driven decision-making, and catalyzing innovation.

    Diverse career opportunities await individuals equipped with Data Science training, encompassing roles such as data scientist, data analyst, machine learning engineer, data engineer, business intelligence analyst, among others. These roles are prevalent across various industries, including the technology sector, healthcare, finance, and e-commerce. The demand for data science professionals is on a consistent upward trajectory, offering robust job prospects.

    Data Science unquestionably stands as a trend poised to lead the technology industry in the future. The capacity to extract valuable insights from data and make data-informed decisions is progressively integral to business operations. Data science, along with its subfields like machine learning and artificial intelligence, resides at the vanguard of technological innovation, shaping the future of technology.

    Undoubtedly, there is substantial demand for Data Science training. The increasing recognition of data's pivotal role in strategic decision-making and operational efficiency by organizations fuels the need for professionals well-versed in data science. This demand cuts across diverse industries, rendering data science a highly sought-after domain.

  • Establish your development environment by configuring tools like Python, Jupyter Notebooks, and pertinent libraries (e.g., Pandas, Matplotlib, Scikit-Learn).
  • Procure and engage with real-world datasets, which can be sourced from platforms like Kaggle or government data repositories.
  • Commence with data cleansing and preprocessing to render the data amenable to analysis.
  • Engage in data visualization experiments to glean insights and foster a deeper understanding of the data.
  • Implement statistical analysis and machine learning algorithms to fabricate predictive models.
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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 Science Courses
  • Hands-On Experience
  • Certification Exam
  • Prerequisites

    • Obtaining a Data Science certification can enhance your job prospects, though it doesn't guarantee employment. However, it does enhance your resume's appeal to potential employers and increases your chances of securing a position in the field. It demonstrates your commitment to learning and your expertise in Data Science concepts and tools.

    Yes, you can pursue multiple Data Science course certifications. Many professionals opt for multiple certifications to diversify their skill set and bolster their marketability. Nonetheless, it's crucial to strike a balance between pursuing certifications and gaining practical experience through projects.

    Various types of Data Science certifications are available, including vendor-specific certifications (e.g., Microsoft Certified Data Scientist), university-affiliated certifications (e.g., Stanford Data Science Certification), and those offered by professional organizations (e.g., SAS Certified Data Scientist). Each type has its unique focus and requirements.

  • Validation of Skills
  • Career Advancement
  • Salary Improvemen
  • Competitive Edge
  • Professional Networking

    • For beginners, a suitable Data Science certification might include options like the "IBM Data Science Professional Certificate" on platforms such as Coursera, or a similar introductory program covering fundamental topics and tools.

  • Data Scientist
  • Data Analyst
  • Machine Learning Engineer
  • Business Intelligence Analyst

    • Yes, you can often take online certification tests for Data Science. Many certifying bodies and platforms offer online proctored exams, providing individuals with the convenience of completing the certification process from their own location. Make sure to review the specific certification's requirements and online testing options.

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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

    Varun Fresher

    Data Scientist | Bangalore

     Got 10 LPA Package

    More Details : Acte offers an excellent Data Scientist training program. I had no past expertise in this sector as a freshmen, yet the lecturers made the difficult topics understandable. I am currently working as a Data Scientist at a top IT company thanks to the great professional advice and help I received. Regards, Acte!

    Nivetha Fresher

    Data Analyst | Chennai

     Got 10 LPA Package

    More Details : When I first participated in the Acte Training Institute's Data Analyst program, it completely changed my life. The teachers were very knowledgeable, and the course material was extensive. I now have real-world experience thanks to the practical assignments, and I work for a reputable organization as a data analyst. Acte was a major contributor to my profession in data analysis.

    Vicky Fresher

    Data Engineer | Hyderabad

     Got 8 LPA Package

    More Details : Acte's Data Engineering program was a great choice for me as a fresher. The hands-on labs and real-world projects gave me the practical skills I needed. Thanks to Acte, I now work as a Data Engineer, handling complex data infrastructure and pipelines.

    100% Placement

    7000+ Placed Student

    600+ Hiring Partners

    5.5 LPA Average Salary

    Recently Placed Students

    Akshara
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    John
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    Data Science Training FAQ's

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

    Data science is a multidisciplinary field that encompasses the utilization of diverse methodologies, algorithms, procedures, and systems to extract valuable insights and knowledge from data. It involves a broad spectrum of tasks, including data collection, data cleansing, data analysis, data visualization, as well as the application of machine learning and statistical techniques to facilitate data-driven decision-making and predictions.

  • Python or R
  • Expertise in data analysis and visualization
  • A deep understanding of machine learning and statistics
  • Domain-specific knowledge
  • Competence in data engineering
  • Effective communication abilities

    • While data science and traditional statistics share common ground, they diverge in their focal points and methodologies. Traditional statistics predominantly concentrates on data analysis through hypothesis testing and inference, typically utilizing structured data in a limited context. In contrast, data science encompasses a broader data spectrum, including unstructured data, and relies on a wide array of tools, machine learning techniques, and data-driven decision-making processes to derive insights and predictions from data.

  • Data Collection
  • Data Cleaning and Preprocessing
  • Exploratory Data Analysis (EDA)
  • Feature Engineering
  • Model Construction (Machine Learning)
  • Model Assessment and Validation
  • Data Visualization
  • Communication of Findings
  • Deployment and Monitoring (as applicable)

    • Data scientists commonly employ programming languages such as Python and R for tasks related to data analysis, machine learning, and data manipulation. Python is especially favored for its extensive libraries (e.g., NumPy, Pandas, Scikit-Learn) and adaptability. R is often chosen for statistical analysis and data visualization.

  • Machine Learning: Encompassing a broad spectrum of algorithms, machine learning enables the discovery of patterns within data and the creation of predictive models.
  • Deep Learning: Deep learning constitutes a specific category within machine learning, emphasizing neural networks with multiple layers (deep neural networks).
    • Show More

    Online data science learning refers to the process of acquiring knowledge and skills related to data science through internet-based platforms, courses, and programs. It differs from traditional in-person education in that it allows students to access learning materials, lectures, and assignments remotely, often at their own pace.

    Online data science courses are typically delivered through e-learning platforms or educational websites. They involve video lectures, reading materials, assignments, quizzes, and sometimes interactive elements like discussion forums.

  • Flexibility.
  • Accessibility.
  • Diverse Resources.
  • Cost-Effective.
  • Real-World Focus.
  • Self-Paced Learning.
  • A basic understanding of mathematics and statistics.
  • Familiarity with programming (e.g., Python or R).
  • Some courses may require prior experience with data analysis tools or software.
  • Certain advanced courses might have prerequisites like intermediate statistics or programming skills.
  • Define your goals
  • Review course content
  • Check the instructor's credentials
  • Consider accreditation
  • Read reviews and testimonials
  • Evaluate the learning platform

    • Computer and software prerequisites for online data science education can fluctuate based on the particular course or program. In a broad sense, learners are advised to possess a contemporary computer with sufficient processing capabilities and memory to support data science software tools. An internet connection holds paramount importance for accessing course materials, streaming video lectures, and actively participating in online learning activities. Typically, a modern web browser is a requisite for accessing course websites and online learning platforms.

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    Data science can play a significant role in facilitating data-informed decision-making and strategic development in diverse ways. It empowers organizations to amass, scrutinize, and interpret substantial datasets to uncover trends, patterns, and invaluable insights.

  • Clarify Objectives and Goals
  • Collect and Integrate Data
  • Cleanse and Preprocess Data
  • Conduct Exploratory Data Analysis (EDA)
  • Engage in Feature Engineering
  • Undertake Model Development and Training
  • Perform Model Assessment and Validation
  • Employ techniques such as data anonymization or pseudonymization to shield individuals' identities.
  • Adhere to pertinent data protection regulations (e.g., GDPR, HIPAA) by following correct consent and data handling protocols.
  • Implement robust security measures for safeguarding data storage and transmission.
  • Conduct periodic audits and reviews of data handling procedures to remain in adherence with regulatory standards.

    • The cost associated with the implementation of data science solutions can exhibit substantial variation, contingent upon project intricacy, data volume, and requisite tools. While initial expenses may encompass hardware, software, and expertise, the anticipated return on investment emanates from enhanced decision-making capabilities, cost reductions, revenue augmentation, and the amplification of operational efficiency.

    The collection and preparation of data entail the identification of data sources, data cleansing, transformation to address missing values, and the assurance of data quality. This process also encompasses data integration, where information from diverse sources is harmonized into a consolidated dataset suitable for in-depth analysis.

  • Programming Languages
  • Data Storage
  • Data Analysis
  • Machine Learning
  • Data Visualization
  • Big Data Handling
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