Softlogic is one of the Top Training institutes for Data Science. Learn how to use Data Science, from beginner basics to advanced techniques. Our Data Science Syllabus Covers Python programming, statistics, data wrangling, data visualization, machine learning, deep learning, and big data analytics. We offer a Data Science Course with Placement Assistance, Mock interviews, Resume building, and certification.
2 Hours Real Time Interactive Technical Training
1 Hour Aptitude
1 Hour Communication & Soft Skills
(Suitable for Fresh Jobseekers / Non IT to IT transition)
4 Hours Real Time Interactive Technical Training
(Suitable for working IT Professionals)
Steps in developing a Machine Learning application
The Data Science Training will cover all the topics ranging from fundamental to advanced concepts, which will make it easy for students to grasp Data Science. The Data Science Course Curriculum is composed of some of the most useful and rare concepts that will surely give students a complete understanding of Data Science as well. So, some of those curriculum are discussed below as objectives:
Data science integrates various techniques and methods to derive insights from data. It involves collecting, cleaning, and analyzing data, using statistical and machine learning methods, and creating visual representations. Key objectives include predicting trends, summarizing past data, recommending actions, and understanding causes. Applied across sectors like healthcare, finance, and retail, data science drives decision-making and solves complex problems.
A Data Science Full Stack encompasses all tools, techniques, and technologies needed for a complete data science project, from data acquisition to application deployment. It includes data collection, storage, cleaning, and preprocessing, as well as exploratory analysis, statistical modeling, machine learning, and visualization. Additionally, it covers big data technologies, deployment, and ongoing monitoring to ensure a comprehensive data workflow.
The following are the reasons for learning Data Science:
The following are the prerequisites for learning Data Science:
Our Data Science Course Fees may vary depending on the specific course program you choose (basic / intermediate / full stack), course duration, and course format (remote or in-person). On an average the Data Science Course Fees range from 30k to 40k, for a duration of 3 months with international certification based on the above factors.
The following are the jobs related to Data Science:
The following are the Data Science real-time applications:
Python and R are the main programming languages used in Data Science. Python is popular due to its ease of use, rich ecosystem of libraries (like Pandas, NumPy, and Scikit-learn), and flexibility. R is recognized for its robust statistical analysis features and is widely used in academic and research settings.
Missing data can be managed through various approaches, including imputation (substituting missing values with mean, median, or mode), utilizing algorithms designed to handle missing data, or by excluding records or features with missing values. The choice of method depends on the extent and nature of the missing data.
Supervised learning involves training models on datasets with labeled responses, aiming to predict outcomes for new, unseen data. In contrast, unsupervised learning involves working with unlabeled data to uncover hidden patterns or groupings.
Feature engineering is the process of creating or modifying features to enhance a machine learning model’s performance. It is vital because the effectiveness of features directly influences the accuracy and efficiency of the model.
For classification tasks, metrics such as accuracy, precision, recall, F1 score, and ROC-AUC are commonly used. For regression tasks, metrics like mean squared error (MSE), mean absolute error (MAE), and R-squared are often employed.
To evaluate model performance, relevant metrics (for classification or regression) are used, along with cross-validation to ensure the model generalizes well to new data. Additionally, examining residuals or errors can help understand the model’s limitations.
Hyperparameter tuning involves adjusting the settings of a machine learning algorithm that are not learned from the data but are predefined before training (such as learning rate or the number of trees in a forest). The objective is to identify the optimal set of hyperparameters to enhance model performance.
Big data technologies such as Hadoop and Spark are designed to handle and analyze large-scale datasets efficiently. Hadoop provides a framework for distributed storage and processing, while Spark offers rapid, in-memory data processing capabilities, which are crucial for managing extensive data volumes.
The corporate office of the Softlogic Systems is located at K.K.Nagar.
Softlogic accepts a wide range of payment methods, including:
The following are the scopes available in the future for learning the Data Science Course:
