Key Learning Objectives�
At�Bright Computer Education, we provide quality training with moderate fees. Furthermore, our trainers are certified experts with their hands on real time projects. We also assist in placement and interviews. For this purpose, we particularly have a team of HR experts to help you. Furthermore, there is also time flexibility at our Institute. We provide classes on weekdays and also on weekends along with fast track training classes.
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Overview of data analytics, its importance in decision-making, and key concepts such as data types, sources, and formats.
Selecting, transforming, and engineering features to improve the performance of machine learning models.
Techniques for collecting, cleaning, and preprocessing raw data to prepare it for analysis.
Evaluating model performance, selecting appropriate evaluation metrics, and validating models using cross-validation techniques.
Analyzing and visualizing data to uncover patterns, trends, and insights using statistical and graphical methods.
Analyzing time-series data, forecasting future trends, and detecting seasonality and trends using statistical methods and models.
Calculating and interpreting descriptive statistics such as mean, median, mode, variance, and standard deviation.
Analyzing unstructured text data, performing sentiment analysis, and extracting insights from text using natural language processing (NLP) techniques.
Understanding probability distributions, hypothesis testing, confidence intervals, and regression analysis for making inferences about data.
Introduction to big data technologies such as Hadoop, Spark, and Apache Kafka for processing and analyzing large-scale datasets.
Creating effective visualizations such as histograms, scatter plots, box plots, and heatmaps to communicate insights from data.
Understanding data governance principles, privacy regulations, and ethical considerations in data analytics.
Building predictive models using techniques such as linear regression, logistic regression, decision trees, and random forests.
Applying data analytics techniques to real-world business problems and case studies to demonstrate practical applications.
Introduction to machine learning algorithms such as k-nearest neighbors, support vector machines, and clustering algorithms.
A Data Analytics training program offers comprehensive training in data analysis techniques, statistical methods, machine learning algorithms, and data visualization tools for extracting insights from data.
This program is ideal for aspiring data analysts, business analysts, data scientists, and professionals seeking to enhance their skills in data analysis and decision-making using data-driven approaches.
The program covers data collection, preprocessing, exploratory data analysis, descriptive and inferential statistics, data visualization, predictive analytics, machine learning, time series analysis, text analytics, big data analytics, and ethical considerations.
While prior programming experience can be helpful, the program is designed to accommodate learners with varying levels of experience, from beginner to advanced.
Graduates can pursue careers as data analysts, business analysts, data scientists, machine learning engineers, and consultants in industries such as finance, healthcare, retail, marketing, and technology.
Yes, many platforms and institutes offer online Data Analytics courses, which you can take from anywhere. Some popular online platforms include: Udemy, Coursera, edX, and LinkedIn Learning Local institutes also offer online courses with interactive sessions, assignments, and certification. Online learning allows you to study at your own pace and schedule, with access to resources like recorded lectures, assignments, and projects.